Systems and methods for panel design in flow cytometry

ABSTRACT

Embodiments of the present invention encompass systems and methods for determining detection limits for various antibody-dye conjugates for flow cytometry. Exemplary techniques involve a linear superpositioning approach of spillover-induced enlargements of normally distributed measurement errors.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present disclosure claims priority to U.S. Provisional PatentApplication No. 61/791,492 filed on Mar. 15, 2013 which is herebyincorporated by reference.

BACKGROUND OF THE INVENTION

Embodiments of the present invention relate generally to systems andmethods for evaluating cells of a biological sample, and in particularto techniques for selecting antibody-dye conjugate combinations for usein flow cytometry. Further embodiments relate generally to automatedsystems and methods for analyzing positivity in multicolor flowcytometry.

Cell surface immunophenotyping using fluorescent flow cytometry hasbecome a relatively routine process for differentiating and countingcells of interest in a cell sample containing many different cell types.Typically, cell surface probes, e.g., fluorochrome-labeled monoclonalantibodies (MABs) or other suitably labeled ligands, specific toantigens on the outer surface of the cells of interest, are used toselectively tag or “stain” such cells for subsequent detection. The flowcytometer operates to detect the stained cells by irradiating individualcells in the sample, one-by one, with radiation specially adapted toexcite the fluorochrome labels. When irradiated, the labels fluoresceand their associated cells scatter the incident radiation in a patterndetermined by the physical and optical characteristics of the irradiatedcell. Suitable photo-detectors within the flow cytometer detect thescattered radiation and fluorescence, and their respective outputsignals are used to differentiate the different cell types on the basisof their respective light-scattering and fluorescence signatures.

Immunophenotyping by flow cytometry typically involves the selection ofa set of probes or reagents physiologically appropriate for the desiredevaluation or monitoring procedure. Relatedly, because certain diseaseconditions can be characterized by the expression of various antigens onthe surface of cells or inside the cells of the patient, antibody probereagent panels can be selected which correspond to such antigenprofiles. For example, the Solastra™ 5-Color Reagent Panel is a panel ofconjugated-antibody cocktails for use in characterizing hematolymphoidneoplasia by flow cytometry. The panel can be used to identification andenumerate relevant leukocyte surface molecules, and as aid in thedifferential diagnosis of patients with certain abnormal hematologyresults and/or presence of blasts in the blood stream, bone marrow,and/or lymphoid tissues. Solastra™ 5-Color Reagents are composed ofantibodies directed to B, T, and Myelomonocytic lineage antigens. Suchpanels can be used in flow cytometric analyses for hematopathologyapplications.

The measurement of samples run through a flow cytometry device yields acharacteristic photonic signature of scattered light, fluoresced light,or a combination thereof. By analyzing the signature, it is possible toinfer physical and chemical characteristics of the particle. Oftenprotein expression, a biological feature of an exemplary particle, issubject to the interrogation. The particle signatures from a sample ofblood can be displayed in a dot plot, and gating can be used tointerpret those signatures. Generally, gating is used to classify asignature as either positive or negative. For example, gating can beused to determine whether a particle is a blood cell or a piece ofdebris, or whether the blood cell contains a marker for disease. Hence,gating is important for diagnostic and clinical hematology applications.However, it can be difficult to determine whether a particle belongs toa positive or negative population, such as when the positive andnegative signatures have a similar appearance. A variety of gating orspecificity control techniques, such as isotype controls, modelsapplying cluster analysis algorithms such as principal componentanalysis, and fluorescence minus one (FMO) have been proposed to helpdetermine whether a particle should be classified as either positive ornegative.

Although currently known antibody panel selection techniques providemany benefits to those who perform cell evaluation and monitoringprocedures, still further improvements are desired. Further, gatingcontrol techniques to evaluate samples can be improved. Embodiments ofthe present invention provide solutions to at least some of theseoutstanding needs.

BRIEF SUMMARY OF THE INVENTION

Embodiments of the present invention encompass systems and methods forselecting and simulating antibody-dye conjugate panels for use in flowcytometry, and other related cellular evaluation and monitoringtechniques. Often, such panels can be used or designed for evaluatingcells of a biological sample. Such cells may be obtained from anindividual person, from a cell culture, from a pool of human ornon-human donors, or the like. According to some embodiments, thetechniques disclosed herein can be used to evaluate any material thatincludes particles (e.g. biological cells) which are present in asuspension and which have structures on their surface or their insidethat may be recognized by specific biological fluorochrome-labeledprobes that non-covalently bind to these structures such as antibodies,toxins, receptor ligands, or derivatives thereof or similar compounds.As discussed elsewhere herein, exemplary probes, which may includefluorochrome-labeled monoclonal antibodies (MABs) or other suitablylabeled ligands, can be specific to antigens on the outer surface of, oron the inside of, the cells of interest. Although difference samplepreparation procedures may be used depending on whether the analysisinvolves external or internal antigens, the data acquisition techniquesdiscussed here apply equally to either type of analysis. Suitablephoto-detectors within the flow cytometer detect the scattered radiationand fluorescence, and their respective output signals are used todifferentiate the different cell types (or subtypes of a certain celltype or different functional statuses among a certain type of cells) onthe basis of their respective light-scattering and fluorescencesignatures. These fluorescence signatures can be resolvedcomputationally by a procedure referred to as fluorescence compensationthus delivering quantitative information on the presence of eachinterrogated single antigen on the surface or inside the cell/particle.

Multi-color immunophenotypic analysis using flow cytometry typicallyinvolves using panels or cocktails of antibody-dye conjugates. Thepanels can be configured so that individual probes, having respectiveindividual dyes, correspond to individual color detection channels of aflow cytometry device. As discussed herein, the selection of probepanels can be automated, thus streamlining the multi-color flowcytometry analysis process. The use of such probe panels in flowcytometry can provide for the efficient acquisition of excellent qualitydata using multiple detection channels. Hence, downtime can be reducedand lab productivity can be maximized. Relatedly, embodiments of thepresent invention provide an improvement of sensitivity for thedetection of prioritized antigens, and an enhanced facilitation of dataanalysis.

Exemplary flow cytometry devices may include various laserconfigurations (e.g. multiple solid state lasers) providing excitationspectra corresponding to red, blue, violet, yellow, and the like.Interchangeable optical filters can be used to facilitate the detectionof a variety of dyes and wavelengths. Exemplary systems can be used foranalyzing multiple fluorescent markers simultaneously. For example,systems having six fluorescence detectors can provide simultaneousacquisition of up to six fluorescence signals. Additional fluorescencedetectors and/or lasers can be added to a system, enabling concurrentreading of up to ten or more colors. Embodiments of the presentinvention provide graphic display techniques for providing a user withplots, charts, and other visual features which can facilitate theanalysis of complex flow cytometry data.

In one aspect, embodiments of the present invention encompass systemsand methods of determining a probe panel for analyzing a biologicalsample in a flow cytometry procedure. Exemplary methods includeinputting a flow cytometer hardware configuration, inputting a rostercomprising a plurality of probes, where individual probes of the rosterare associated with respective individual channel-specific detectionlimits, inputting an antigenic coexpression pattern, and determining theprobe panel based on the flow cytometer hardware configuration, theindividual channel-specific detection limits, and the antigeniccoexpression pattern. The probe panel may include a subset of probesfrom the roster.

Further embodiments of the present invention encompass systems andmethods for assessing positivity in multicolor flow cytometry. Exemplaryspecificity or gating control techniques can be used to evaluate anindividual particle signature, for example to determine whether a bloodcell is positive or negative for a certain protein expression, such as adisease marker. In some cases, these control techniques can be used toposition a gate or graphical region relative to acquired data, so as toclassify the cells from which the data is obtained. Exemplary controltechniques can be used in multicolor procedures following compensation.In some cases, the methods disclosed herein provide for a level ofstandardization which is not present in currently used techniques.Moreover, the control techniques disclosed herein are time-efficient,economical, effective for heterogeneous expression patterns, and providefor the quantification of positives.

The above described and many other features and attendant advantages ofembodiments of the present invention will become apparent and furtherunderstood by reference to the following detailed description whenconsidered in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of systems and methods according to embodiments of the presentdisclosure are described by the illustrations and figures set forthbelow.

FIG. 1 depicts aspects of flow cytometry systems and methods accordingto some embodiments.

FIG. 1A depicts an exemplary hardware illustration of a flow cytometrydevice, having a three laser, ten color filter block configuration,according to some embodiments.

FIG. 1B depicts aspects of a probe panel selection technique, accordingto some embodiments.

FIG. 1C depicts aspects the expression of antibody-dye conjugates, whichmay refer to a set of antibody-dye conjugates within a databasecontaining information regarding flow cytometry probes, according tosome embodiments.

FIG. 1D depicts aspects of database queries for antigen-specificantibodies selected by a user to return information associated with therespective probes, according to some embodiments.

FIGS. 1E-1M depict aspects of a panel evaluation technique, according toembodiments of the present invention, according to some embodiments.

FIGS. 1N-1O depict examples for associations and calculations for threeconjugates, to associate given CD with a given dye, according to someembodiments.

FIGS. 1P-1U depict further aspects of a panel evaluation technique,according to embodiments of the present invention, according to someembodiments.

FIGS. 1V-1W depicts aspects of displays for results for exemplary tencolor probe panels. according to some embodiments.

FIG. 2 depicts aspects of an operator selection procedure, according tosome embodiments.

FIG. 2A-2C depict operation selection features for target phenotype,phenotype exclusion, and parent/descendent schemes, respectively,according to some embodiments.

FIG. 2D depicts operation selection features for antigen densityparameters, according to some embodiments.

FIG. 3 depicts aspects of a probe panel selection technique, accordingto some embodiments.

FIG. 4 certain aspects of isotype signaling and resolution sensitivity,according to some embodiments.

FIG. 5 depicts a comparison between coefficience of variance andstandard deviation for expression events, according to some embodiments.

FIGS. 6A-6B depict aspects of a bimodal distribution for determiningnegative signal events, according to some embodiments.

FIG. 7 depicts aspects of a broadened distribution of signal events,according to some embodiments.

FIG. 8 depicts aspects of real data results from a staining protocolusing a single antibody-dye conjugate, according to some embodiments.

FIG. 9 depicts aspects of detection limits that are can be independentof, or adjusted for, compensation factors, according to someembodiments.

FIG. 10 depicts aspects of an exemplary spillover pattern distortionmatrix for certain dyes, according to some embodiments.

FIG. 11 depicts aspects of a coexpression matrix, according to someembodiments.

FIGS. 12A-12I depict schematics of estimated staining patterns,according to some embodiments.

FIG. 13 depicts aspects of probe panel evaluation including thecategorization of expression patterns, according to some embodiments.

FIG. 14 depicts aspects of probe panel evaluation including relativefluorophore contribution, according to some embodiments.

FIG. 15 depicts aspects of probe panel evaluation including relativeexpression brightness of dyes, according to some embodiments.

FIG. 16A depicts an exemplary schema for a probe panel system, accordingto some embodiments.

FIGS. 16B-16C depict aspects of a user input module for a probe panel,according to some embodiments.

FIGS. 16D-16E depict aspects of simulator graphic modules for expressionrelationships in tabular form, according to some embodiments.

FIGS. 16F-16G depict aspects of simulator graphic modules for predictedresult profiles, according to some embodiments.

FIGS. 16H-16J depict aspects of a simulator numerics module fordistortion calculations, according to some embodiments.

FIGS. 16K-16L depicts aspects of spillover pattern modules, according tosome embodiments.

FIGS. 16M-16O depict aspects of an antibody database module, accordingto some embodiments.

FIG. 17 depicts aspects of a numerical approach to model spilloverpatterns including detection radar graphics for multivariate analysis,according to some embodiments.

FIGS. 18A-18B depict further aspects of a numerical approach to modelspillover patterns including detection radar graphics for multivariateanalysis, according to some embodiments.

FIGS. 19A-19B depict further aspects of a numerical approach to modelspillover patterns including detection radar graphics for multivariateanalysis, according to some embodiments.

FIGS. 20A-20G depict aspects of a computerized interface for designingand simulating a probe panel, according to some embodiments.

FIGS. 21A-21B depict further aspects of a computerized interface fordesigning and simulating a probe panel, according to some embodiments.

FIGS. 22A-22B depict aspects, according to some embodiments.

FIGS. 23-26 depict aspects of a computerized interface for designing andsimulating a probe panel, according to some embodiments.

FIG. 27 depicts a three-dimensional graph modelling distortion in an PMTchannel caused by the intensity of signal of two dyes that the PMT isnot directed to detecting, according to embodiments.

FIG. 28 depicts an exemplary distortion table, according to someembodiments.

FIGS. 29-29E depict aspects of real data, plotting event data acquiredfrom a flow cytometry instrument applying gating to the data whereapplicable, according to some embodiments.

FIGS. 30-30A depict aspects of real data, plotting event data acquiredfrom a flow cytometry instrument without applying gating to the data,according to some embodiments.

DETAILED DESCRIPTION OF THE INVENTION

Flow cytometry often involves labeling a particle sample withfluorochrome dyes, and then evaluating properties of individualparticles of the sample using various fluorescence detectors specificfor various wavelengths. In this way, it is possible to obtainquantitative and qualitative data about the particle sample. Forexample, different cell surface receptors on a blood cell can be labeledwith different fluorochrome dyes, and a flow cytometer can use separatefluorescence channels to detect the resulting light emitted. Inexemplary embodiments, multiple excitation light wavelengths can be usedin conjunction with multiple fluorochrome dyes and multiple fluorescencedetectors, so as to simultaneously obtain several parameters of asample. In particular embodiments, distortion factors resulting from theconjunctive use of multiple fluorochrome dyes and multiple fluorescencedetectors can be quantified.

The term “event” as used herein can refer to a particle as it passesthrough a light beam, or the data or signature representing theparticle. An event can be evaluated using multiple detectors, and eachdetector can provide respective intensity or signal parameter.Relatedly, each detector can be associated with a respective channel ofthe flow cytometer. For example, a measurement from an individualdetector can be referred to as a parameter (e.g. forward scatter, sidescatter, or fluorescence measured) and the data acquired in eachparameter for a particle can be referred to as an event.

In some cases, a measured parameter may not reach a particular thresholdfor the detector channel, and hence may not register as an event. Inthis sense, the interaction between a light beam and a particle flowingthrough the cytometer may or may not produce a particle event.Optionally, such a threshold can be used to reduce or eliminate signalscaused by noise, debris, and the like.

Embodiments of the present invention encompass systems and methods thatinvolve determining detection limits of fluorescence signals inphotomultipliers for use in flow cytometer applications. In some cases,the determination of a detection limit can be based on an expectedexpression pattern of a target cell (which can be labeled withantibody-fluorochrome conjugates), the expected fluorescence signalintensities for individual fluorescent labels arising from thefluorescent labeling of the antigens comprised by the expressionpattern, and on an expected spillover matrix for the fluorochromes inthe different photomultipliers.

According to some embodiments, there may be an additional input whichencompasses the data spread that is specific for a given wavelengthdetection range (as determined by the bandpass filter in front of aphotomultiplier) as the latter can determine the respectivephotomultiplier sensitivity and hence the measurement error (dataspread). The relation between spillover and resulting data spread can beassessed experimentally.

Exemplary embodiments allow a cytometry device user to select a desiredfluorochrome antibody combination (e.g. probe panel) which can be usedto build flow cytometer experiments including a prediction of detectionlimits for the fluorochrome conjugates; this can also be referred to asa panel simulation. Furthermore, a cytometry device user may select acombination of antibodies without assigning fluorescent labels to eachof these, respectively, in order to obtain a proposal for a probe panelwith minimized detection limits for desired single probes within thisprobes panel. In some instances, panel evaluation or design techniquescan involve the use of a linear superpositioning model ofspillover-induced enlargements of normally distributed measurementerrors.

Probe panel evaluation and design techniques as disclosed herein can usedata from reference fluorochrome measurements with a single dye forcalculations of a spillover (which can be alternatively referred to asoverspill/overspilling, spilling, or crosstalk). In some cases, adistortion factor can be characterized by the following formula:

$\frac{\begin{matrix}\left\lbrack {{increase}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {detection}\mspace{14mu} {limit}\mspace{14mu} {in}\mspace{14mu} a\mspace{14mu} {non}\text{-}{primary}\mspace{14mu} {channel}} \right\rbrack \\\lbrack{decades}\rbrack\end{matrix}}{\left\lbrack {{intensity}\mspace{14mu} {in}\mspace{14mu} {the}\mspace{14mu} {primary}\mspace{14mu} {channel}} \right\rbrack \lbrack{decades}\rbrack}$

In other words, the determination of a distortion factor can quantifythe spillover effect of a first label (which is part of a probe-labelconjugate), where the first label is intended or configured to bemeasured in a first channel (e.g. a PMT detector), into at least asecond channel, where the second channel (or other additional channels)is intended and configured to measure a different label. In someaspects, the distortion factor can be an estimate of an increase indetection limit in the second channel as a function of an emissionintensity of a first probe-label combination. In other aspects, thedistortion factor can be a linear function of the emission intensity ofa first probe-label combination. In further aspects, the distortionfactor can be calculated using a crosstalk index. In some aspects, thedistortion factor is mathematically modified by a coefficientrepresenting the coexpression pattern of antigens corresponding to afirst probe-label combination and a second probe-label combination, thesecond probe-label combination being intended or configured to bemeasured in a second channel. In further aspects, determining adistortion factor for each label in a first potential probe panel can bedone to calculate a total increase in detection limit in a secondchannel.

In some instances, a probe panel evaluation or design technique caninvolve the use of an expected expression pattern of antigens ondifferent target cells, such as exclusion, subpopulation (e.g.parent-descendent as depicted in FIG. 1D), non-exclusive co-expression,or expected antigen expression characteristics such as discrete ormodulated. In some cases, a probe panel evaluation or design techniquecan involve predicting, estimating, or otherwise determining detectionlimits for individual detection channels, optionally for a variety ofdifferent cell types or antigen expression patterns.

As used herein, in the description of antigens, the geneological terms“parent”, “child”, “sibling”, “aunt”, and “cousin” are used to identifyand describe relationships between clusters of differentiation (“CD”)for specific antigens, or the corresponding antibody. It is noted,however, that in the context of the present disclosure, these terms donot refer to generations of cellular reproduction, but rather refer todevelopmental steps of a given cell as antigens on the given cellsurface change due to differentiation or specialization. For example, anantigen such as CD45 may be present on an native T-cell; if the nativeT-cell specializes to become a killer T-cell, the CD45 antigen may actas a precursor to and become a CD2 (child) antigen, whereas if thenative T-cell specializes to become a helper T-cell, the CD45 antigenmay act as a precursor to and become a CD4 (child) antigen. In such asituation, the CD45 antigen can be referred to as the parent antigen toboth sibling CD2 and CD4 antigens.

Possible developmental relationships between antigens are generally setforth as follows. On some cells, two different antigens may occur on thesame cell surface, each of them with its typical mean density and rangeof expression, resulting in a case called “co-expression”. The presenceof an antigen on the cell surface may exclude the presence of anotherspecific antigen on the same cell surface, resulting in a case called“exclusion”. Antigens with an identical “parent” are called “siblings”.On some cells a first antigen can occur on a cell surface where a secondantigen is also expressed, however, the second antigen may also occur ona cell surface without the first antigen being present. In such cases,the second antigen is called “parent” antigen while the first antigen iscalled “descendant” or “child” antigen, resulting in a case called“parent-descendant” or “parent-child”. The “sibling” of a “parent” of agiven antigen is called the “aunt” of that antigen. When considering aspecific antigen, its non-excluded “siblings”, its children, theirfurther children (or “grandchildren”), further descending antigens, its“parent”, their further “parents” (or “grandparents”) and all furtherascending antigens and its non-excluded “aunts” can be referred to asthe developmental genealogy (or developmental tree) of the specificantigen. It may occur that a single protein is expressed on differentcell types belonging to different developmental genealogies, possiblyalso bearing different mean density, range and distributioncharacteristics of expression of this antigen. The multiple cellularentities of this multiply expressing antigen can be referred to as“multiple”. Further, two co-expressed antigens may have inverselycorrelated densities of expression, resulting in a case called “inversecorrelation”.

A set of guidelines for creating developmental genealogies for any givenantigen can include the following rules: (1) each antigen must beassigned at least one “parent”, if the “parent” is not known “unknown”is assigned as “parent”; (2) if existing, for each antigen “exclusions”,“correlations” or “inverse correlations” with “siblings” must beidentified, in aspects, the order of consideration of siblings can bebased on which antigen has the alphabetically preceding name; (3) ifexisting, for each antigen “exclusions”, “correlations” or “inversecorrelations” with “aunts” must be identified, unless the aunts areexcluded by the parent of the respective antigen; (4) “multiples” mustbe handled as is if they were multiple different antigens in order tomaintain consistency of individual antigen genealogies, this is done byassigning different “parents”, excluded or inversely correlated“siblings” and “aunts”, and (5) correlated antigens share the sameexclusions and inverse correlations but do not necessarily have the samemultiples. By this set of rules, the full set of antigen developmentalgenealogies for a certain species and body compartment can be created,establishing all possible relations between all antigens.

According to some embodiments, detection limits can be used to rank,compare, or otherwise evaluate fluorochrome-antibody panels, based onantigen expression patterns (which may involve antigen expressiondensities and fluorochrome brightness), optionally in combination withexperimental data. In some aspects, calculations related to determiningdetection limits can include inputting, or receiving informationregarding, a maximum expected signal of a first probe-label combination,based on the characteristics of a first probe and first label. In aninstrument or system where signal from a first probe-label combinationcan create spillover into at least a second detection channel (i.e. achannel not configured to specifically detect the first probe-labelconjugate), calculating the increase in detection limits of a secondchannel can be based on a distortion fact and the maximum expectedsignal of the first probe-label combination. Accordingly, a probe-labelcombination can be selected or chosen to include probe panel based onthe calculated increase(s) in detection limit(s). In further aspects,the increase in a detection limit in at least a second channel can becaused by an increase in measurement error, as a function of emissionintensity, of a first probe-label combination.

Exemplary probe panel systems and methods as disclosed herein are wellsuited for use in evaluating various conjugate (e.g.fluorochrome-labeled antibody, panel-label combinations, etc.)combinations having complex spillover patterns. Such systems and methodscan involve the use of detection channel and expression pattern specificdetermination of a typical signal intensity and an increase in detectionlimit related to a typical signal intensity. Accordingly, systems andmethods may operate based on particular parameters such as expressiondensities, fluorochrome intensities, coexpression patterns, anddistortion factors. In some instances, the evaluation of a probe panelmay include determining a maximum difference for a typical signalintensity and for an increase in detection limit. In some cases, theevaluation of a probe panel may involve determining the ratio of atypical signal intensity to a detection limit, for one or moreparticular antibody-dye conjugates. Exemplary probe panel evaluationsystems and methods can be based on the consideration of coexpressionpatterns and on the simulation of experimental results. The selection ofprobe-label combinations can be further based on a comparison of thecalculated total increase of detection limit, with an expected minimumincrease in at least a second channel. Further, a total increase indetection limits, for all channels, can be calculated for each probe inone or more potential probe panels. In some aspects, systems and methodsfor evaluating various conjugate combinations can further includecalculating a total increase in detection limit for each probe in asecond potential probe panel and selecting the probe panel based on acomparison of the calculated total increase in detection limit for eachprobe in the first potential probe panel, with the calculated totalincrease in detection limit for each probe in the second potential probepanel. In other aspects, such methods or systems can include calculatinga total increase in detection limit for each probe in a second potentialprobe panel and selecting the probe panel based on the calculated totalincrease in detection limit for a prioritized probe in the firstpotential probe panel and the second potential probe panel. In otheraspects, a first channel and a second channel can be adjacent channels.

Further, in some embodiments, methods of designing a probe panel for aflow cytometer can include: identifying a first probe and a secondprobe; identifying an expected minimum signal of the first probe;determining a first detection limit of the first probe based on apotential label associated with the second probe; determining a seconddetection limit of the first probe based on a different potential labelassociated with the second probe; and selecting which label to associatewith the second probe for the probe panel based on the first detectionlimit, the second detection limit, and the expected minimum signal ofthe first probe. In aspects, determining a first detection limit caninclude multiplying a distortion factor by a maximum expected signal ina detection channel intended to measure the potential label associatedwith the second probe. In other aspects, determining the first detectionlimit can include multiplying the distortion factor by a coefficientrepresenting an antigenic coexpression pattern, where in some cases, thecoefficient is either one or zero. In further aspects, a maximumexpected signal can be based in part on any or all of an expectedantigen density on a target cell, the potential label associated withthe second probe, and an antigenic coexpression pattern. Someembodiments of such methods or systems include a first probe which isintended to be detected in a first channel, and where determining afirst detection limit of the first probe includes multiplying adistortion factor by a maximum expected signal for each channel of theflow cytometer other than the first channel. In such aspects,determining the first detection limit of the first probe is based on alinear superpositioning model of CV enlargements. In some aspects, thedistortion factor can be a measure of CV enlargement caused by colorcompensation.

In some embodiments, a method of designing a probe panel for a flowcytometer can include: identifying a first probe, a second probe, and athird probe; identifying a plurality of possible probe panels, eachpossible probe panel including a combination of the first probe, thesecond probe, or the third probe, each probe having a possible labelassociated thereto; evaluating a first possible probe panel bydetermining the detection limit of the first probe based on spectrumspillover effects of combination of the second probe and its associatedpossible label; evaluating a second possible probe panel by determiningthe detection limit of the second probe based on spectrum spillovereffects of the combination of the third probe and its associatedpossible label; and selecting the probe panel from the plurality ofpossible probe panels based on the detection limits determined. In suchaspects, at least one of the first probe, the second probe, and thethird probe can specifically bind to an antigen. In similar aspects, atleast one of the first probe, the second probe, and the third probe canspecifically bind to an analyte. In further aspects, evaluating a firstpossible probe panel includes determining the detection limit of a firstprobe based in part on a coexpression pattern of antigens associatedwith the first probe and a second probe. In some aspects, thecoexpression pattern of antigens can include information regardingcoexpression relationships between antigens for a particular cell type.In aspects, spectrum spillover effects of combinations of a second probeand its associated label can be determined to be zero if thecoexpression pattern of antigens associated with a first probe and thethe second probe indicate the probes are mutually exclusive. In furtheraspects, spectrum spillover effects of combination of a second probe andits associated label can be determined to be zero if the antigenassociated with the second probe is a descendent of the antigenassociated with a first probe. In aspects, spectrum spillover effects ofcombination of the second probe and its associated label can bequantified as a function of a distortion factor and an antigeniccoexpression pattern. Conjuntively or alternatively, spectrum spillovereffects of combination of the second probe and its associated label canbe quantified as a function of an expected antigen density on a targetcell. In some aspects, such methods or systems can include displaying agraphical representation of a population distribution of expectedsignals for a pair of probes in a selected probe panel, where displayingthe graphical representation of the population distribution can includedisplaying the determined detection limit of the first probe and thesecond probe.

The probe panel techniques as disclosed herein are well suited for usewith various automated devices, including the Navios™ and Gallios™ FlowCytometry systems (Beckman Coulter, Brea, Calif., USA). In some cases,distortion calculations can be based on specific properties orperformance characteristics of filter sets and/or photodetectors (e.g.PMTs). Further, probe panel techniques can be based on spectralproperties of dyes, optionally which are available within a particularlibrary or repository of dyes or antibody-dye conjugates.

In some instances, the evaluation or simulation of probe panels can bebased on certain antigen expression profiles or patterns. Such profilesor patterns may or may not be associated with a particular cell type. Inoperation, a user may select antibody-dye conjugates according to aparticular expression pattern, which may be a selected target expressionpattern in a planned experiment. In some cases, an instrument such as aflow cytometer may be configured to accept multiple colors (e.g. tencolors) and the user may wish to chose a lesser number of probes. Hence,the user may expressly assign a first number of probes, while leaving asecond number of probes as a dummy variable, so that the sum total ofprobes is equivalent to the number of color channels in the flowcytometer. In some cases, different dyes (e.g. PC5 and PC5.5) may bedetected at the same channel and therefore may not involve simultaneousapplication.

As disclosed elsewhere herein, once a user selects or inputs certainparameters of a probe panel, the system can operate to retrieve orupload part numbers or other identifying indicia from an antibodydatabase module. If, for example, a desired conjugate (i.e. probe) isnot contained in the library, the part number (PN) can be indicated as acustomer design service (CDS) probe. In some cases, systems and methodsinvolve the evaluation of a probe panel based on a respectivefluorochrome property and a signal intensity (e.g. assumed) that couldbe expected based on an expression density of the targeted antigen, andsuch parameters can be retrieved or read from an antibody databasemodule. In some cases, an antibody database module may include dataconcerning various parameters for use in evaluating probe panels,including probe signal intensity, which may be based on an antigendensity for the probe specificity in conjunction with conjugatedfluorochrome. Conjugate intensity data can be based on estimates, or maybe based on experimental data, for example which may be obtained as partof a manufacturing quality control process.

Further embodiments of the present disclosure encompass systems andmethods for assessing positivity in multicolor flow cytometry. Exemplaryspecificity or gating control techniques can be used to evaluate anindividual particle signature, for example to determine whether a bloodcell is positive or negative for a certain protein expression, such as adisease marker. In some cases, these control techniques can be used toposition a gate or graphical region relative to acquired data, so as toclassify the cells from which the data is obtained. Exemplary controltechniques can be used in multicolor procedures following compensation.In some cases, the methods disclosed herein provide for a level ofstandardization which is not present in currently used techniques.Moreover, the control techniques disclosed herein are time-efficient,economical, effective for heterogeneous expression patterns, and providefor the quantification of positives.

In some cases, the emission spectra measured from different fluorescentdyes may overlap, and it may be helpful to compensate the signalsobtained by the detectors. For example, a fluorescence compensationtechnique can be applied during data analysis so as to determine howmuch interference that Fluorochrome A is having in Channel B (which isassigned to specifically measure Fluorochrome B). As a result, it ispossible to obtain the total measured fluorescence at Channel B, andsubtract the contribution of Fluorochrome A, so as to determine thefluorescence of Fluorochrome B at Channel B. According to someembodiments, it is possible to obtain the total measured fluorescence atChannel B, and eliminate the contribution of Fluorochrome A, so as todetermine the fluorescence of Fluorochrome B at Channel B, for examplewhich may be accomplished using a matrix-based compensation approachinvolving digital compensation.

Event data can be visually depicted in a variety of ways. For example, ahistogram can be used to display a single measurement parameter (e.g.fluorescence) on the horizontal X-axis and the number of events (e.g.cell count) on the vertical Y-axis. In this way, it is possible todetermine the number of cells in a sample having certaincharacteristics. For example, a short peak on the left side of the graphmay represent a small group of cells having a dim fluorescence (eventswithin a negative population) and high peak on the right side of thegraph may represent a large group of cells having a bright fluorescence(events within a positive population).

As used herein, a “gate” can be used as a boundary to differentiatebetween a positive population and a negative population. Similarly, agate can be used as a boundary to define a subpopulation of events. Agate can be set, for example, by delineating a boundary around a subsetof events on a data plot such as a dot plot or histogram. A gate can beinclusive so as to select events that fall within a boundary, orexclusive so as to select events that fall outside of the boundary.Accordingly, the number of positive events (on a particular side of theboundary) can refer to the number of cells displaying a physical featureor marker of interest. According to some embodiments of the presentinvention, gating can be used to distinguish signals corresponding tofluorescent objects from signals corresponding to non-fluorescentobjects. According to some embodiments, any event detected with aphotomultiplier tube (PMT) may emit a fluorescence signal. Hence, anemitted fluorescence can be associated with a specific label.

Specific gating protocols are available for diagnostic and clinicalpurposes in the hematology field. For example, gates can be used in flowcytometry data to selectively visualize certain cells of interest suchas white blood cells, while eliminating results from unwanted particlessuch as dead cells and debris. In some situations, it can be difficultto determine where to place a gate so as to effectively classify anevent as either positive or negative. By using an appropriate control,it is possible to help identify the difference between a positivepopulation and a negative population. Embodiments of the presentinvention can be used in conjunction with multicolor cytometrytechniques in general, including without limitation the hematologicalfield. In some cases, embodiments of the present invention can beapplied to any measurement where a Fluorescence Minus One (FMO) controlis helpful or necessary.

Embodiments of the present disclosure provide systems and methods forconducting automated positives analysis in multicolor flow cytometrythat is characterized by spillover-induced enlargement of measurementerrors. Embodiments further encompass techniques to calculatemeasurement errors according to each individual event's expressionpattern based on a linear superpositioning model of fluorochromespillover, as also applied for panel design and simulation applications,using the same or similar techniques for determining prediction limitsfor multicolor flow cytometry, taking into account co-expression patternof antigens on a detected particle so as to allow for the determinationof a spillover ratio for a fluorochrome into all other detectionchannels by a superposition calculation.

Relatedly, embodiments of the present invention encompasspost-acquisition correction techniques for flow cytometry, such thatcompensation errors associated with acquired sample results can beminimized. For example, use of standard compensation approaches inmulti-color channel experiments with fixed correction values can tend toeither overcompensate or undercompensate the experiment results at agating border. Embodiments of the present invention encompass the use ofcorrectly compensated data, or avoid such overcompensation orundercompensation,

In some cases, the automatic correction techniques disclosed herein canoperate to improve compensated results in a flow cytometry device, so asto enhance the identification of specific positive results after dataacquisition. In some embodiments, systems and methods disclosed hereincan operate to calculate a corrected detection limit or correctedboundaries between two result sections or corrected limits betweenspecific positivity and specific negativity for each fluorescence signalchannel (e.g. photomultiplier), based on a superpositioning model of thespillover from all other dyes detected on all other channels. Using suchcorrected limits between specific positivity and negativity, it ispossible to proceed automatically without the manual gating of resultsets, and critical results at the border area between two resultsections are observed to be unambiguously assigned to the appropriatesection.

Overview

Turning now to the drawings, FIG. 1 depicts aspects of flow cytometrysystems and methods according to some embodiments. The configuration 100of a flow cytometry device typically includes certain fluorescencesignal detector assembly parameters 110, as well as laser excitationwavelength parameters 120.

Flow cytometry devices can be configured with any of a variety of laserexcitation parameters. For example, laser assemblies can be configuredto produce excitation spectra at 355 nm (ultraviolet), 405 nm (violet),488 nm (blue), 532 nm (green), 561 (yellow), 633 nm (red), 638 nm (red),and the like. Relatedly, laser assemblies can include any number oflaser excitation devices. For example, a dual laser assembly may includea first laser for delivering excitation energy at 488 nm and a secondlaser for delivering energy at 638 nm.

As shown in FIG. 1, the laser excitation energy can impinge upon a dyeof an antibody-dye conjugate probe. Typically, a particular dye will beexcited at a characteristic wavelength, and subsequently fluoresce acharacteristic emission spectra. There may be one or several maxima offluorescence emission. The emission spectra can cover a range ofwavelengths. For example, fluorescein isothiocyanate (FITC) is afluorochrome that is excited by 488 nm light and that produces afluorescence emission maximum around 520 nm. At least one wavelength,PC5 and PC5.5 for instance are excited by two wavelengths 488 and 638 nmwhich adds complexity to the spillover patterns.

Signal detector assemblies 110 can include various combinations offilters and detectors. As shown here, a detector assembly may include a525/40 bandpass filter for use with a photomultiplier tube device PMT1that is designated for detecting FITC dye emission. Such configurationscan be designed to provide desired detection parameters for a particularfluorochrome. Further configurations as shown can include: a 575/30bandpass filter for use with a photomultiplier tube device PMT2 that isdesignated for detecting PE dye emission; a 620/30 bandpass filter foruse with a photomultiplier tube device PMT3 that is designated fordetecting ECD dye emission; a 675/20 bandpass filter for use with aphotomultiplier tube device PMT4 that is designated for detecting PC5dye emission; a 695/30 bandpass filter for use with a photomultipliertube device PMTS that is designated for detecting PE-Cy7 dye emission;and a 660/20 bandpass filter for use with a photomultiplier tube devicePMT6 that is designated for detecting APC dye emission. Individualdetectors (e.g. PMT1, PMT2, PMT4) can be designated to detect lightwavelengths from respective primary dyes (e.g. FITC, PE, PC5) asindicated by emission spectra graph 130. Bandpass filters can be used toallow certain amounts of emitted light to pass therethrough, and ontoward a photomultiplier tube (PMT). Specific PMT with their associatedbandpass filters may be dedicated to detecting the emission ofconjugates excited by specific wavelengths of light.

An exemplary hardware illustration of a flow cytometry device, having athree laser, ten color filter block configuration, is depicted in FIG.1A. As shown here, a flow cytometry device can include a variety ofbandpass (BP) filters, as well as dichroic (DC) short pass (SP) and longpass (LP) filters.

With returning reference to FIG. 1, it can be seen that exemplarytechniques may involve a user or operator performing certain actions.For example, user actions 140 may include determining or selecting aconfiguration of a flow cytometry device, as indicated in step 142, anddetermining or selecting an antigenic expression pattern of interest, asindicated in step 144. With regard to the step of selecting a deviceconfiguration, such a selection can be made using a dropdown (database)button, to choose from a variety of preset hardware configurations (e.g.such as the hardware configuration represented in FIG. 1A).

According to some embodiments, the selection of various hardwareconfigurations and the selection of antigen expression patterns ofinterest may be implemented in a web-based version. According to someembodiments, systems and methods may involve database means which canprovide an indication if there are potentially more antigens out of theprobes panel that are included in the expression pattern than the userassigned to a certain pattern. If so, this can result in pointing out tothe user that there are more antigens including these probes' influenceon the detection limits.

According to some embodiments, hardware configuration may represent anyof a variety of laser assembly, filter set, and detection channelcombinations. For example, a particular hardware configuration mayinclude one or more lasers emitting at various wavelengths. In somecases, a hardware configuration may include six detection channels andtwo laser colors (e.g. 488 nm and 638 nm). In some cases, a hardwareconfiguration may include eight detection channels and two laser colors(e.g. 488 nm and 638 nm). In some cases, a hardware configuration mayinclude ten detection channels and three laser colors (e.g. 405 nm, 488nm, and 638 nm). In some cases, a hardware configuration may include tendetection channels and four laser colors (e.g. 405 nm, 488 nm, 561 nm,and 638 nm).

Exemplary cell monitoring procedures involve evaluating individual cellsfor the relative quantitative presence or absence of certain cellsurface or internal antigens. As shown in FIG. 1, a particular cell at acertain stage of development or in a certain disease state may present adistinctive antigenic expression pattern 150 characterized by thepresence of certain antigens (e.g. CD28, CD26, CD3, CD15, CD59, CD71) atthe surface of the cell or inside of the cell. Hence, an antibody-dyeconjugate panel containing probes specific for such antigens (e.g.conjugate specific for CD28, conjugate specific for CD26) whichrespectively produce specific emission spectra upon excitation, can beused to analyze a biological sample to determine the extent to which thesample contains cells expressing such antigens, and also the relativequantity of antigen expression on expressing cells. In this way, theprobe panel can be used to evaluate or monitor the physiological statusof a patient. FIGS. 1B to 1H provide additional details concerning theprocessing of the user input, the return of database entries andsubsequent calculations, and the interpretation of displays.

FIG. 1B depicts aspects of a probe panel selection technique accordingto embodiments. As shown here, a user can input a set of antibodiescorresponding to a particular antigenic expression pattern of interest(e.g. CD57, CD45, and other cell markers). In some cases, the selectedantibodies may be assigned to or associated with respective predefineddyes. According to some embodiments, a user may assign each specificityto a predefined dye. According to some embodiments, a user may have theopportunity to enter the specificity only and to let the softwarepropose the optimal antibody-dye-assignments. The selection of probepanels can further include “dummy” channels, where a particular channeland related dye are not intended to be used, the dummy designation andpanel design can be used as a “silent” channel and negative control. Asused herein, a “silent” channel indicates a detector channel that causesno spillover of signal into any other channel, and can be referred to asa “clean column” when viewed as part of a distortion table. Similarly,as used herein, an “untouched” channel indicates a detector channel thatdoes not receive any spillover of signal from the dyes that channel isnot configured to detect, and can be referred to as a “clean row” whenviewed as part of a distortion table.

As shown in FIG. 1B, the use of PC5.5 as a dye for detection of the CD33antigen in detection channel FL4 would be complicated by use of PC5 orthe attempted detection of PC5 in detection channel FL4. Accordingly,PC5 is assigned as dummy in this probe panel. FIG. 1B further shows thata user can assign a specific antigen to a desired dye in a particulardetection channel, particularly in the example shown, Pacific Blue dyeconjugated to CD57, which will be excited by light at 405 nm anddetected in PMT detection channel FL9. In this context, the FL9 is theprimary channel for detection of excited Pacific Blue dye (and thereforedetection of CD57), and accordingly, FL1-8 and FL 10 are secondarychannels that could detect unwanted spillover fluorescence signal fromthe Pacific Blue dye.

As illustrated in FIG. 1C, a probe panel, which may refer to a set ofantibody-dye conjugates, can be linked with a database containinginformation regarding the probes. For example, the database may containinformation concerning the fluorescence intensity for each probe in thepanel. In some embodiments, the term “panel” may also refer to asequence or group of several conjugate combinations. As shown, (anddiscussed in further detail with regard to FIG. 8 below) the databasecan provide information regarding a set of conjugates (also referred toas the panel), that allows for the calculation of the typical meanfluorescence intensity of a bright positive population, as indicated bypopulation events in decades above 10{circumflex over ( )}0 as shown inthe graphs, identified as section (A) in FIG. 1C. The database can alsoallow for calculation of the minimal fluorescence intensity of a dimpositive population. In a discrete antigen expression characteristicevaluation, as seen with the CD4-PE graph, the positive and negativepopulations are clearly separated, and the minimal fluorescenceintensity of the dim positive population is identical to 1. In amodulated antigen expression characteristic evaluation as seen in theCD184-PE versus CD57-PCB graph, however, positive and negativepopulations are not clearly separated, as the antigen densities can varybetween the positive cells, and the minimal fluorescence intensity isassumed to be within the zero decade (i.e., less that 10{circumflex over( )}0). Further, the database can allow for calculation of the increaseof a detection limit for a conjugate (and the corresponding loss ofsensitivity) in deaces for each conjugate resulting from the combinedeffects of all occurring spillover contributions, identified as (B) inFIG. 1C. In some aspects, the distortion factor of one conjugate ontoanother can be characterized as the ratio of (B) over (A).

Relatedly, as shown in FIG. 1D, based on the set of antigen-specificantibodies selected by the user, which can be referred to as the input160, it is possible to query a database 162 for information associatedwith the respective probes. As discussed elsewhere herein, thecalculations 164 discussed in relation to FIG. 1D can be used forreference for example with respect to the techniques depicted in FIG. 8.Database queries 162 can on the user input 160 of a conjugate caninclude, for example: (C) the intensity (in decades over 10{circumflexover ( )}0) of a PE-conjugate (or other reference dye) for eachconjugate; (D) the fluorochrome brightness relative to PE for eachconjugate, where PE (or other reference dye) is equal to 1 or 100%; (E)the typical increase of detection limit caused by secondary channels perdecade of signal intensity in a primary channel for each fluorochromeuse, i.e. a distortion factor; (F) typical expression characteristics ofeach antigen, evaluated as either discrete or modulated; (G) the typicalantigen coexpression patterns for each single antigen with all of theantigens, i.e. the coexpressions of interest, where “1” codes forcoexpression and “0” codes for an absence of coexpression or exclusion;and (H) the typical parent-descendent antigen coexpression patters foreach single antigen with all other antigens, where “0” codes for adescendent property and “1” codes for an absence of a descendentproperty.

The output 166 of a database as described in FIG. 1D depends on the areaof its application. Particularly, the assumed cell type of interest isdifferent for immunomonitoring and blood cell disorders, thereforetypical coexpression patterns and typical expression characteristicsrelated thereto vary depending on the cell type of interest.Calculations 164 based on data retrieved from the database due to searchqueries can provide the following output. A value for (I), the typicalmean fluorescence intensity above 10{circumflex over ( )}0 for eachconjugate for bright positive populations (thus directed to discreteplots), can be calculated as:

(I)=log(((10{circumflex over ( )}(C)−10{circumflex over( )}0*(D))+10{circumflex over ( )}0)

where the addition and subtraction of 10{circumflex over ( )}0 providesfor consistency in a plot scaling. A value for (J), the typicalfluorescence intensity of each conjugate for dim positive populations(thus directed to modulated plots) based on expression characteristics,is similar to the equation for (I) above, but with the value for (C) setto 0 decades above 10{circumflex over ( )}0.

The value for (K), the increase of detection limit (DL) in a secondarydetection channel through spillover of a single conjugate in a primarychannel, can be determined as:

(K)=(I)*DF*(G)*(H)

where the value of (K) is calculated for each single conjugate detectedin a primary channel, which for example can be nine (9) values for (K)calculated for secondary channels in a ten (10) color panel. Further, avalue (L) for the overall increase of DL in a secondary detectionchannel through combined spillover of all conjugates detected in theirrespective primary channels can be given by:

(L)=log((10{circumflex over ( )}(K ₁)−10{circumflex over( )}0)+log((10{circumflex over ( )}(K ₂)−10{circumflex over( )}0)+log((10{circumflex over ( )}(K _(n))−10{circumflex over ( )}0)

where, again, the addition and subtraction of 10{circumflex over ( )}0provides for consistency in a plot scaling. An (L) value can becalculated for each secondary channel. The results of such calculations164 can subsequently be output 166 to either or both of a display andfurther processing.

FIG. 1E depicts aspects of a panel evaluation technique, according toembodiments. The graphic display charts shown here can be useful inanalyzing or ranking certain panel designs. As shown here, a certainprobe or stain is provided, having an antibody specific for the CD45antigen, conjugated with a PE fluorophore dye. The PE dye can be excitedby a 488 nm laser, and can emit spectra which is detected on an FL2channel (e.g. at approximately 575 nm). The CD45 antigen is typicallystrongly expressed on leukocyte cells, and PE is a bright fluorophore.Hence, it is possible to observe a strong signal intensity when applyinga CD45-PE probe to a lab sample of human origin. As shown here, theresult is a full scale intensity. The dashed (minimum separation, squaredata symbols) and dotted (maximum separation, triangle data pointssymbols) lines coincide, indicating a discrete expression. That is, thepositive and negative populations are clearly separated.

FIG. 1F depicts the addition of a CD25-ECD probe. The ECD dye also canbe excited by a 488 nm laser, and can emit spectra which is detected onan FL3 channel (e.g. at approximately 625 nm). As shown here, CD25 is amodulated antigen. That is, there is a difference between the dashed(minimum separation) and dotted (maximum separation). Depending on theactivation status of the cell, cells can have a high expression of CD25,down to a negative expression of CD25. Here, the dashed line (minimumseparation) coincides with the limit between the first and seconddecades. This corresponds to a desired detection limit, whereby cellswith a very low expression (dim signal) can be detected, as well ascells with a higher expression (bright signal). As depicted here, thecells with a higher expression (maximum separation; dotted line) areabout 0.75 Decades higher than cells with a lower expression (minimumseparation; dashed line). The difference (A) between the minimum(dashed) and the maximum (dotted) represents the range of expression forCD25 according to this embodiment. Note that this data can also takeinto account parameters associated with the ECD dye itself. The lowerpanel bubble plot of FIG. 1F indicates that emission spectra from the PEdye is spilling over into the ECD detection channel and causes anestimated increase of detection limit according to the position of thecircle center when projected to the y-axis (in decades) (FL3).

As discussed elsewhere herein, such plots can include different linescorresponding to different laser configurations (e.g. 405 nm, 488 nm,and 638 nm). The threshold between the first and second decades can beused to evaluate certain signal data. In some cases, a particularbackground may be assumed for a particular coexpression pattern or celltype. For example, a T cell may have a certain pattern as compared to aB cell. Hence, a user may select a phenotype (and correspondingcoexpression pattern) that they may wish to see in a simulation output.In some cases, for a given expression pattern, detection limits for aone particular cell type may be different from detection limits foranother particular cell type, for example depending on the presence orabsence and quantitative characteristics of the given expressionpattern.

As shown in FIG. 1F, a detection limit can be represented by a dashedline (square data point symbols). In cases where expressioncharacteristics are discrete, there may be either a negative population,or, positive population, but nothing in between. That is, there will beno cells with varying degrees of antigen densities on that particularpopulation. Hence, the dashed and dotted lines will coincide (as withCD45-PE at about 575 nm). At that point, the highest expression densityis equal to the lowest expression density. In contrast, with a modulatedexpression, an antigen may be found on a particular cell type at a verylow expression density, up to a very high expression density. As shownhere, a modulated expression can be represented by a dotted line. Forexample, on a particular CD3+ cell type, CD4 may either be present(CD4+, CD8−), or absent (CD4−, CD8+). This represents a strongpositivity, and corresponds to a discrete expression characteristic. Asdiscussed elsewhere herein, the probe panel evaluation can also be basedon particular phenotypes (e.g. by assuming a default phenotype such as Tcell or monocyte). Hence, users can evaluate probe panel characteristicsaccording to different cell types and expression patterns, and determinehow expression patterns may affect detection limits for particularantigens.

In contrast to FIG. 1F where the CD25 probe has an ECD label, FIG. 1Gdepicts results from a CD25 probe having a stronger fluorochrome, PC5.5.The desired detection limit of FIG. 1G is the same as that shown in FIG.1F. However, the range (A) between dotted (maximum) and dashed (minimum)which the embodiment assumes, for the dynamics of the signal intensity,is much higher in FIG. 1G. As shown here, the range is about 1 Decade.The lower panel bubble plot of FIG. 1G indicates that emission spectrafrom the PE dye is spilling over into the PC5.5 detection channel (FL4).

In FIG. 1H, the CD25-ECD probe is reintroduced. Hence, it is possible tocompare the highest expression (signal intensity) that could be expectedfor a CD25-ECD label with highest expression density that can beexpected for CD 25-PC5.5 label. Assuming again, a logarithmic scale, thelatter is considerably larger.

It is also useful to consider the background (solid line). For example,as shown in FIG. 1H, a certain level of background BG may occur due tothe lymphocyte expression high levels of CD45, which is tagged with theCD45-PE label. As shown here, that background BG signal from PE on theECD channel is higher than the signal from ECD on the ECD channel.Hence, there is a significant loss of information, and such a paneldesign may not be favorable.

In FIG. 1I, the CD25-PC5.5 probe is removed and a CD25-PC7 probe isadded. There is spillover from PE and PC7 into the FL3 channel. In FIG.1J, the CD45-PE probe is removed, and there is spillover from PC7 intothe FL3 channel. A favorable result is provided when the solid line(background, BG) coincides with or approaches the threshold between thefirst and second logarithmic decades, as shown in FIG. 1J. As shownhere, there is not an overwhelming amount of background that is causedby spillover into the ECD channel. Rather, there is a small amount ofspillover from the CD45-PC7 probe emission onto the ECD FL3 detectionchannel. Hence, there is a gap between the background (BG) and thehighest expression density (maximum; dotted line). Accordingly, thedetection schema loses only those cells having very low levels of CD25expression.

FIG. 1K depicts the replacement of the CD25-ECD probe of FIG. 1J with aCD25-PE probe. The PE fluorochrome is stronger than the ECDfluorochrome. As shown in FIG. 1K, there is a similar background BGprovided by the CD45-PC7 probe on the PE detection channel FL2. However,the distance between the background and the maximum is greater in FIG.1K, as compared with FIG. 1J, due to the stronger PE fluorochrome on theCD25 probe.

In FIG. 1L, the CD25-ECD probe is again included in the panel. Here, itcan be seen that the distance between the background (solid) and themaximum (dotted) signal is greater for CD25-PE than it is for CD25-ECD.Hence, the overall sensitivity is greater. This result takes intoaccount the intensity of the fluorochrome used on the CD25 probe (e.g.PE intensity>ECD intensity), and also the spillover pattern that stemsfrom other antigens (e.g. CD45) which are also expressed on the cells.Both the fluorochrome intensity and the coexpression spillover patterncan contribute to the sensitivity.

In FIG. 1M, a CD45 antibody is conjugated with an APC-AF750 fluorophoreand it can be seen that there is no spillover. Here, the solid line (BG)coincides with the threshold between the first and second decades, andhence there is no background added. Here, there is full sensitivity, sosignals can be detected even where there are cells with low expressiondensities for CD25. As discussed elsewhere herein, as more probes areadded to the panel, more complex results will be observed. Such resultscan take into account various contributions (e.g. fluorophore intensity)where the coexpression pattern is relevant to the spillover.

FIGS. 1N and 1O provide an example for three conjugates, where a givenCD can be associated with a given dye. As shown in FIG. 1N, each CD-dyeconjugate has a MFI for PE-conjugation above 10{circumflex over ( )}0decades (PE being the reference dye); as shown CD-X has an MFI of 0.5,CD-Y has an MFI of 1, and CD-Z has an MFI of 2.5. In some embodiments,the parent descendent matrix can be symmetric. In some embodiments, theparent descendent matrix can be asymmetric. As shown here, thecoexpression matrix can be symmetric, although in other aspects, thecoexpression matric can be asymmetric. Further, as shown in FIG. 1O,each dye of the CD-dye conjugates has a relative intensity compared toPE; the A-dye has an intensity of 0.2, the B-dye has an intensity of0.45, and the C-dye has an intensity of 0.85. These values of the givenCD-dye conjugates allow for calculation of a distortion factor table asseen in FIG. 1O, where, in-part, the B-dye has a distortion value of0.25 effecting the PMT FL1 that is directed to detecting A-dye, and hasa distortion value of 0.75 effecting the PMT FL3 that is directed todetecting C-dye. In contrast, the C-dye has no distraction effect on thePMT FL1, and similarly, the A-dye has no distortion effect on the PMTFL3 that is directed to detecting C-dye. In further contrast, the A-dyehas a distortion value of 0.65 and the C-dye has a distortion value of0.1 on the PMT FL2 that is directed to detecting the B-dye. Calculationsshown in FIG. 1O are applications of the equations as given by FIG. 1D,using the relevant values provided in FIGS. 1N and 1O.

Where the coexpression pattern has a certain parent/descendent scheme,or an exclusion scheme, then the calculated spillover may not add to theoverall distortion. For example, FIG. 1P depicts the calculated resultfor a single stain (CD25-PE probe). When a second stain (CD45-FITC) isadded to the panel, as shown in FIG. 1Q, it can be seen that the FITCdye emission exerts spillover onto the PE FL2 detection channel. Thecircle on the bubble plot corresponding to the FITC spillover has acertain intercept on the Y-axis, and a certain diameter. As shown here,the CD45-FITC probe can take away about half of a decade of sensitivityon the PE FL2 channel. For example, the distance between the backgroundand the maximum in FIG. 1Q is about half of that distance as depicted inFIG. 1P.

In FIG. 1R, the CD45-FITC probe of FIG. 1Q is replaced with a CD15-FITCprobe. The CD15 antigen is less highly expressed, and hence the signalat the FITC detection channel is lower in FIG. 1R as compared to FIG.1Q. For instance, it can be seen that the maximum (dotted line) is nolonger between the third and fourth decades. However, CD45 iscoexpressed with CD25, whereas CD15 is not coexpressed with CD25 (e.g.according to default settings in the database). Hence, there is noeffective distortion caused by the CD15-FITC probe spillover onto theFL2 PE channel. Relatedly, there is no addition to the background at thePE channel. In other words, the spillover still is present but does notaffect CD15 negative cells with regard to analysis of their potentialCD25 expression. CD15+ cells, in this example, do not express CD25.Hence, based on the biology of the cells, CD25 expression would not beanalyzed on CD15+ cells. These two different antigens could thus beevaluated using different gates.

In another illustration of a coexpression schema, FIG. 1S depictsresults where both a CD3-FITC probe and a CD45-ECD probe are generatingspillover spectral emission to the PE FL2 detection channel. The overalldistortion is a little more than half of a decade. Such a result may beconsistent with a coexpression pattern observable on a T cell. It canalso be seen that there is a minor amount of spillover into the FL1 FITCdetection channel. The bubble plot is useful in resolving thecontributions. For example, when considering the Y-intercept valuescorresponding to the spillover at the FL2 channel, it can be seen thatthe CD45-ECD spillover is greater than the CD3-FITC spillover. Therespective bubble diameters at the FL2 channel indicate that ECDprovides a greater contribution to the overall distortion.

Hence, to improve the signal at the FL2 channel (e.g. increasesensitivity), it may be more desirable to remove that ECD spillover atFL2 (e.g. while retaining that FITC spillover). FIG. 1T depicts such aresult, where the CD45-ECD probe of FIG. 1S is replaced with a CD45-APCprobe. Hence, there is a considerable improvement at the FL2 PE channelfor the detection limit. That is, there is a greater distance betweenthe background (solid) and maximum (dotted) lines.

In FIG. 1U, the CD3-FITC and CD45-APC probes of FIG. 1T are replacedwith CD3-APC and CD45-ECD probes, respectively. As shown in FIG. 1U,there is still about a half of decade of signal loss at the FL2 PEchannel.

The bubble plots provide a useful indication of how a probeconfiguration (e.g. antigen specificity, fluorophore) can contribute tothe detection limit for a detection channel.

The display of results for an exemplary ten color panel probe isdepicted in FIG. 1V. As shown at Section A, for prioritizing conjugatesthat detect antigens having a modulated expression characteristic, alarge distance between an estimated mean fluorescence intensity(triangle) and an overall increase of DL (circle) may be desirable, andcan serve as a criterium for the ranking of different conjugatecombinations for identical sets of antigens. As shown at Section B, theestimates on bright (triangle) and dim (box) mean fluorescenceintensities can be based on typical antigen expression densities andrelative fluorochrome intensity (e.g. =(I) & (J) as depicted in FIG.1D), and for example can coincide due to discrete expressioncharacteristics for CDxy-APCA750-conjugate detected on respectiveprimary channel FL8, assigned to a red laser. As shown at Section C,there may be an overall increase of DL=(L) for FL8, assigned to a redlaser. As shown at Section D, due to modulated expressioncharacteristics of CDqw-ECD the typical dim expression density(triangle) can be set to zero decades above 10{circumflex over ( )}0.

Another display of results for an exemplary ten color panel probe isdepicted in FIG. 1W. As shown at Section A, the PE-conjugate (seelegend) in this panel causes a 0.6 decade increase in the detectionlimit (DL) above 10{circumflex over ( )}0 on the third detection channelFL3 (e.g. when referring to the intercept on the Y-axis), and diameterof the bubble circle corresponds to the contribution of the PE-conjugateto the overall increase of the detection limit on FL3. Hence, it can beseen that the PE conjugate provides the largest contribution to theincrease in the detection limit at FL3. As shown at Section B, thePC5/PC5.5 conjugate (see legend) in this panel causes a low-to-moderateincrease of 0.2 decades above 10{circumflex over ( )}0. In many cases,such an increase will be acceptable. Section B also indicates that basedon the magnitude of the bubble diameter, the PC5/PC5.5 conjugate can beconsidered as the major contributor to the overall increase of DL ondetection channel 6.

Hence, in a typical procedure the user may select a particular hardwareconfiguration, which may involve certain filters (e.g. bandpass filters)associated with various detectors. In some embodiments, a particularhardware configuration may be assigned or predetermined, withoutallowing for such a selection. Various hardware configurations may haveassociated distortion factor values. In some cases, the hardwareconfigurations or distortion data may be retrieved from a database. Forexample, a database may include data indicating that a distortion factorfor a particular bandpass detector configuration (e.g. infrared) islarger than some other detector configuration. Accordingly, thesensitivity and/or measurement error can vary based on the hardwareconfiguration. In some embodiments, a database may include a presetnumber of hardware configurations, and the user may select from amongthem. For example, a particular hardware configuration may include datarelated to the Navios™ Flow Cytometry system or the Gallios™ FlowCytometry system (both available from Beckman Coulter, Brea, Calif.,USA). In some cases, a particular hardware configuration will have anassociated distortion factor profile. The number of lasers and/or thenumber of detection channels contained in the hardware configuration canhave an influence on the number of fluorochromes used in a particularprobe panel. In some cases, the characteristics of the bandpass filtersinfluence the distortion factors. Relatedly, the quality of a PMT orother detectors such as an avalanche photodiode may influence thedistortion factors.

Antibody Selection for Panel Design and Simulation

FIG. 2 depicts aspects of an operator selection procedure 200, wherebythe operator inputs certain antibody and dye selection information foruse in selecting an antibody panel. For example, as shown in step 210 a,the operator can select an antibody specific for antigen 1 (e.g. CD28),optionally along with a corresponding dye (e.g. FITC) as indicated instep 210 b. Further, the operator can select additional antibodiesspecific for respective antigens of an expression profile (e.g. steps220 a, 230 a, and 240 a), optionally along with respective correspondingdyes (e.g. steps 220 b, 220 c, 220 d). As shown here, theantigen-specific antibody parameters can be selected from a dropdownmenu of a database, and the dyes can also be selected from a dropdownmenus of a database. In some instances, the dye selection process can beconfigured so that no dye is expressly selected by the operator. Rather,a dye associated with the selected antibody can be provided by thedatabase. According to some embodiments, if the user does not assigndyes to antibodies, then the system can identify the most appropriatedyes for all non-assigned antibodies based on consideration of the wholeprobes panel (sensitivity etc.) out of the database that will provideall conjugations available for the respective antibody. Thefluorochromes chosen by the system may not be displayed until the systemhas conducted the necessary calculation and iterations, i.e. when thesimulated data output occurs.

Systems and methods as disclosed herein can embody the drop downdatabase and user interface features shown here. Hence, the user maydesignate the antigens of interest, and the antigen designation in turnwill influence the antibody selected. According to some embodiments, theuser will directly choose antibodies in the interface. In some cases,the antigens of interest may correspond to a particular cell type, suchas a T cell. Hence, a user may select a collection of antibodies whichcorrespond to a T cell panel.

The menu shown in FIG. 2 includes two columns which can be presented tothe user. The left column corresponds to antibody specificity, and theright column correspond to dye selection or assignment. At this stage inthe process, the user may or may not have a particular expressionpattern in mind. For example, the user may have only a list of antigenswhich may be of interest. In some cases, a user may know when selectingcertain antigens whether it is desirable to be more sensitive whenselecting a dye or alternatively, less sensitive. Relatedly, a databasewill often include data related to the expression density of aparticular antigen, and this data can be taken into account when the dyeis selected or assigned by the system. In some cases, the expressiondensity may depend on a particular cell type. For example, the databasemay include information that a particular marker is minimally expressedon one cell type, whereas the same marker is highly expressed on anothercell type. If a user does not select a dye to go along with a selectedantibody, the system may take into account other features, such as thecell type, when recommending or assigning a dye to that antibody.According to some embodiments, at this stage in the process the systemwill not yet assign a dye in case the user did not select one.

Typically, different dyes will have different brightnesses quantified bythe quantum yield and absorption coefficient of the dye, indicating howmuch of passing light is absorbed and how much of the light absorbed bythe dye will translate to fluorescence emission, respectively. In manycases the absorption coefficient and the quantum yield correspond toeach other. For example, a PE dye is considered to have a highabsorption and quantum yield, and a high percentage of the lightabsorbed is translated to fluorescence emission. In contrast, FITC has alower absorption coefficient and quantum yield, and a lower percentageof the light absorbed is translated to fluorescence emission. As aresult, the selection of a particular dye can determine the sensitivitythat can be achieved. Hence, using PE may confer the ability to achievea higher sensitivity, as compared to the use of FITC (e.g. even takinginto account that a single antibody molecule can be covalently bound toseveral FITC molecules due to the small size of the FITC molecule (<1kD) which cannot be realized for the large (>200 kD) PE molecule).

In some cases, the user may elect to accept one or more of the defaultdye selections provided by the system database. Optionally, the user mayelect to modify the default selections. According to some embodiments,at this stage in the process the system may not recommend dyes to beused with antibodies as the full range of information provided by alluser interfaces input may be needed to select appropriate dyes for theantibodies. Relatedly, a system database may include a certain assumedexpression density for a typical/particular T cell antigen (e.g. CD3antigen). When selecting a particular antigen specificity, the systemmay retrieve a particular expression density that is typical for anantigen on a certain cell type. For example, when selecting an antibodyspecific for a CD3 antigen, the database may include relationshipinformation associating the CD3 antigen with a T cell. As anotherexample, a user may select antibody specificity for CD16, CD38, and thelike. Some antigens have various expression densities, present onvarious types of cells. Hence, expression densities can vary amongst Tcells, B cells, and monocytes, for example. For example, when selectingan antigen specificity, the database may have three typical expressiondensities, for three different cell types. In some embodiments, the usercan opt to modify an assumed or default density, if desired.

FIGS. 2A, 2B, and 2C illustrate similar operation selection features fortarget phenotype, phenotype exclusion, and parent/descendent schemes,respectively. FIG. 2D depicts operation selection features for antigendensity parameters.

As shown in FIG. 2A, the user has the option to define variousphenotypes, if desired, based on the selection of antibodies which maybe contained in a database. In some embodiments, information on typicalantigen coexpression patterns that matches the defined phenotypes can beincluded in a database as preselected or predefined typical antigencoexpression patterns. The database can include default information onthe antigens' coexpression patterns that usually occur. This is lessspecific than predefined cellular phenotypes and allows for combinationsof antigens that do not occur in the same gate. As one example, the Tcell is a prominent type of cell phenotype that is used in cytometry.Optionally, a user may define their own phenotype. In some cases, a usermay assign a certain expression pattern to a phenotype. As shown here,the user also has the option of skipping this step, and not specifying aphenotype. When selecting a particular phenotype, the database canprovide an associated set of antigen specific antibodies. According tosome embodiments, the user can define the phenotype by directly choosingantibodies. For example, if a dye has been assigned to an antibody inFIG. 2 then the formula can display this pre-selection in the rightcolumn in response to choosing the respective antibody in the leftcolumn of FIG. 2A. The column on the right side may allow the user topreselect dyes, in an autofill manner. According to some embodiments, atthis stage in the process the user may not be able to assign dyes toantibodies any more (e.g. as has been done in FIG. 2). However, if theuser has assigned dyes to antibodies in FIG. 2 then these dyes will bedisplayed in the right column upon choosing the respective antibodies inthe left column. In some cases, it is possible to select betweendifferent phenotypes, or to prioritize phenotypes. For example,phenotypes can be prioritized based on a target population, or based ona particular antigen that should be detected on a particular phenotype.In some cases, the system may prioritize an antigen based on expressiondensity. For example, an antigen with a low expression density can begiven a high priority.

As shown in FIG. 2B, the user has the option to select various phenotypeexclusions, if desired, which may be contained in a database. Forexample, a database can contain information indicating that a certainantigen never occurs on the same cell surface with another certainantigen. Optionally, the user may define certain exclusions. As depictedhere in the left column, the user may define a first exclusion, whichcorresponds to an antibody that was selected earlier as discussed inrelation to FIG. 2 and is displayed in the header section of the leftcolumn. The user may select multiple exclusions in this manner, formultiple antibodies of the panel. For instance, the user may indicatethat an antigen CD-X is to be exclusive of antigens CD-Y and CD-Z. Suchselections can have an impact on a graphic display for a probe panel, asdiscussed elsewhere herein. According to some embodiments, the exclusionof a particular antigen may not influence a respective detection limit,because only labels that are on the same cell surface can influence eachother's channel detection limits. That is, once the antigens are not onthe same cell surface, the label dyes will also not be on the same cellsurface, and hence there can be no interference between their respectiveemission spectra. As shown in FIG. 2B, the user also has the option ofnot specifying exclusions.

In some instances, a user may select two antigens that are neverexpressed together. Optionally, the database may make certainassumptions regarding exclusions. For example, the system may assumethat a particular T cell marker and a particular B cell marker areexclusive from one another. In some cases, a system may allow a user toindicate that there are no exclusions. Accordingly, the user may bepresented with various options, including (a) accepting exclusionsprovided by the database, (b) actively assigning exclusions, and/or (c)assigning no exclusions, such that any antigen can occur with anotherantigen, on any cell.

As shown in FIG. 2C, the user has the option to select variousparent-descendent relationships, if desired, which may be contained in adatabase. For example, a database can contain information indicatingthat a certain antigen has a parent or descendent relationship withanother antigen. Optionally, the user may opt not to specify such arelationship. As depicted here in the left column, the user may define afirst parent and any related descendents, which may correspond toantibodies that were selected earlier as discussed in relation to FIG.2. As an example of one parent-descendent scheme, a parent-descendentrelation can mean that a parent marker expressed on parent cell, andthat cell A expresses the parent protein in addition tosubpopulation/descendent A protein, and that cell B expresses the parentprotein in addition to a subpopulation/descendant B protein. Forinstance, a parent T cell may include a CD3 antigen, and some T celldependents may express CD3 antigen along with a child cell antigen CD4,and some T cell dependents may express CD3 antigen along with a childcell antigen CD8. The expression of CD4 and CD8 in the child cells canbe mutually exclusive. For example, the child cells may be eitherCD4+/CD8− or CD4−/CD8+. In some cases, every CD4+ child cell is CD3+,every CD8+ child cell is CD3+. In this way, parent-descendentrelationships can be considered to have an effect on expressionpatterns. Relatedly, parent-descendent relationships can be consideredto have an impact on the relevance of associated distortion. As anexample, if a descendent antigen is labeled with a dye that would causea distortion in the channel where the parent labeled antigen isdetected, then it may not be necessary to account for this distortion.For example, where all CD4+ cells are also positive for CD3, there maybe no CD4+ that will be in the background (e.g. there is no CD4+ that isalso CD3−). In some cases, a parent CD3 label may cause distortion in achild CD4 channel.

Hence, the parent-descendent relationships can have an effect onbackground distortion. Where distortion is applied to a positivepopulation, it could be described as a population that is on a higherrange of the logarithmic scale. In some related instances, such ameasurement error may not be substantially relevant with regard to thedetection limit of the channel in which the positive population isdetected. According to some embodiments, there is no effect caused by aparent-descendant relationship other than making a spillover and relateddistortion irrelevant if the spillover occurs from descendant channel toparent channel. If the spillover occurs from parent into the descendantchannel the “normal” distortion applies.

As discussed elsewhere herein, the primary detection channel for aparticular fluorochrome is the channel where the user intends to detectthe signal associated with that fluorochrome. Relative to a singleprimary channel, there may be one or more secondary channels (e.g. wherea fluorochrome for the primary channel has emission spectra that spillsover into the other channels). For example, a system may be configuredto primarily detect PE dye at an FL2 channel, yet the PE emissionspectra may spillover to other channels, such as FL3, FL4, FL5, and thelike. In this sense, the secondary channels can indicate where unwantedsignal intensities are detected. Such spillover characteristics can beused along with parent-descendent relationships to evaluate or configureprobe panel designs. In some cases, a probe may be assigned to a primarychannel, while having strong spillover to a secondary channel, where thesecondary channel represents a parent channel. As also describedelsewhere herein, parent/descendent schemes for typical populations canbe represented in a database. In some cases, the user may opt to proceedwith a default parent-descendent scheme. In some cases, a user may optto define their own parent-descendent scheme. In some cases, a user mayopt to specify that there are no parent descendent-schemes. For example,with regard to analysis of blood cell disorders, certain expressionpatterns may be unusually aberrant. In such instances, it may bedesirable for the user to indicate that there are no parent-descendentrelationships.

As shown in FIG. 2D, the user has the option to select certain antigendensity parameters, if desired, which may be contained in a database.For example, default antigen densities may be contained in the database.Such defaults may refer to typical populations that are present in humanperipheral blood. Other types of defaults may be implemented. Hence,depending on whether a user is interested in evaluating materialassociated with cell culture cells, or material associated with bonemarrow, for example, different defaults and/or expression densities maybe available.

As also depicted in FIG. 2D, for individual antibodies, it is possibleto select a phenotype as indexed according to FIG. 2A. Each antibodyselected for a particular phenotype can be represented in a histogram.Further, a user may have the option to accept or modify what isconsidered a typical expression density. With reference to the bar belowthe histogram, a button can be moved to shift the positive populationvs. the negative population. Thus, it is possible to influence thesignal to noise ratio that may be assumed for a particular population.Changes in signal-to-noise ration can influence the crosstalk orspillover that may be expected to be present in other channels.According to some embodiments, the terms crosstalk and spillover may beused interchangeably. Hence, where are brighter signal is assumed, orwhere a brighter signal is selected or adjusted, a greater amount ofcrosstalk or spillover may be assumed to be present in other channels.In some instances, a user may opt to proceed with a default (e.g.antigen density and/or signal intensity). In some instances, a user maymodify signal intensities according to the expected characteristics of aparticular desired phenotype, such as a T cell population or a monocytepopulation or a non-human cell population or a population not related tohuman peripheral blood or bone marrow or others.

FIG. 3 depicts aspects of a probe panel selection technique 300according to embodiments. As shown here, data such as user-selectedantibody parameters 310 (e.g. as described in FIG. 2), a flow cytometrydevice configuration 320 (e.g. as described in FIG. 1), and an antibodydatabase 330, can be input into or accessed by a simulator module 340,which operates to proposed optimal combinations according to prioritizedphenotypes and/or antigen expressions, and to produce graphs (e.g. A, B,and C) showing detection limits and population separations based onsimulated probe panels. As illustrated by step 350, it possible toselect a probe panel based on the simulations. In the embodimentdepicted here, the selected probe panel is for flow cytometry deviceconfiguration having three lasers and ten color detection channels (PC5and PC5.5 can be detected by the same channel). The panel includesantibody-dye conjugates specific for ten CD antigens.

Table 1 below depicts aspects of an exemplary probe roster which may beassociated with or part of the database probes. As depicted here,individual probes can include an antibody or other binding agentspecific to a particular antigen, conjugated with a dye.

TABLE 1 Probe Roster Probe Number Dye Antibody Probe 1 Dye (a) Binds toAntigen (i)  Probe 2 Dye (a) Binds to Antigen (ii) Probe 3 Dye (b) Bindsto Antigen (i)  Probe 4 Dye (b) Binds to Antigen (ii)

It is understood that a cellular or surface structure such as a clusterof differentiation (CD) and the antibody directed against the structureor cluster can be referred to interchangeably. For example, an antibodythat recognizes cluster of differentiation 3 (CD3) can also be referredto as CD3 or CD3 antibody. In some cases, the term anti-CD3 antibody maybe used. In some cases, there are cellular structures that have not beenassigned a CD number. In such cases, the name of the structure itselfcan also be used to name the antibody. In some cases, the nomenclatureof an anti-“structure” antibody may be used.

As discussed elsewhere herein, according to an expression pattern thatis retrieved from a database (or assumed or assigned by a user), aparticular expression pattern may correspond to a coexpression scheme, aspecific exclusion scheme, or a parent-descendent scheme. In some cases,selected antigens may be assigned to a certain phenotype. Based on thesecategories of information, it is possible to graphically display data inbivariate dot plot representations such as those shown in Panels A, B,and C. In some cases, the representations may also incorporate estimatesof background distortion.

As shown here, the population X may reflect instances where the intentis to detect antigen X on a particular population. In panel A, there isa sharp hinge of the straight line delimiting the upper left quadrantfrom the upper right quadrant, indicating a strong increase in adetection limit for antigen Y for the population expressing antigen Ydepending on the antigen Y density. These detection limitcharacteristics are very different from that for antigen X for thepopulation expressing antigen Y. The line delimiting the lower rightquadrant from the upper right quadrant has no hinge but is parallel withthe outline of the diagram thus indicating that there is no dependencyof the detection limit for antigen Y from the expression density ofantigen X on respective cells. Hence, this graphical output may indicateto the user that this particular choice may not be optimal.

Hence, where are user selects or inputs for a particular antigenexpression pattern (e.g. as associated with antibody parameters 310),the system may retrieve certain antibody-dye conjugates from a databasebased on the user selection or input. The user can then observe thevarious graphical outputs associated with the antigen expressionpattern. According to some embodiments, the system can generate anoptimized probes panel proposal according to antigen selections made bythe user. According to some embodiments, a user may select a particularantibody specificity profile or antigen expression pattern, and thesystem may return a permutation of all conjugate combinations possiblefor the profile or pattern. In some cases, the system may displayselected conjugate combinations according to prioritized antigens.Hence, there may be a priority ranking for certain antigens, and theranking can be used for determining which conjugate combinations areexpected to deliver optimal results with regard to sensitivity and whichconjugate combinations to display in a graphic output. According to someembodiments, the system can return optimized conjugate combinationsaccording to prioritized conjugates/phenotype related to fluorochromebrightness expression patterns and the like. In some cases, antigens canbe prioritized on the basis of sensitivity. For example, it may bedesirable to have a higher sensitivity for one antigen, while a lowerspecificity for a second antigen is considered as sufficient.

As discussed elsewhere herein, the graphic output can be influenced byvarious factors. For example, the display may depend on the expressionpattern, the flow cytometry device configuration, and/or the probescharacteristics (e.g. whether dyes considered as optimal for a givenspecificity within a given antibody combination, or specificallyselected dyes).

According to some embodiments, some or all entries retrieved from thedatabase may be default. Relatedly, some or all proposals for probespanels as recommended by the system can be calculated based on a wholeset of antigens inclusive of expression patterns. For example, tendefault sets for ten antigens may result in a different probes panelrecommendation when, for example, different expected expression patternsare chosen by the user. The modification of certain parameters, such asan expression pattern or a signal intensity, can result in a change inthe display. In some cases, a simulation display may indicate aparticular recommendation of the system. In some cases, a simulationdisplay may show output according to pre-set dyes. In some cases, asimulation display may show output according to a mixed case, where somedyes are selected by the user and some dyes are selected by the system.According to some embodiments, systems can return optimized conjugatecombinations according to prioritized conjugates/phenotype related tofluorochrome brightness expression patterns and the like. Antigensrelevant for a certain phenotype can be shown on bivariate dot plots orother representations. In FIG. 3, there are three plots shown.Embodiments of the present invention encompass the display of any numberof plots. For example, where five antigens are selected, there may beten plots, as depicted in Table 2.

TABLE 2 1 2 3 4 5 1 — plot 1 plot 2 plot 3 plot 4 2 — — plot 5 plot 6plot 7 3 — — — plot 8 plot 9 4 — — — —  plot 10 5 — — — — —

According to some embodiments, the user can review the display outputplot, and take into account any antigens which have been prioritized,any knowledge of target populations, and/or any knowledge of targetcoexpression antigens, and then evaluate the detection limits anddetermine whether a large distortion is expected or not. In some cases,a system may recommend a particular probe panel, and the user may selectthe recommended panel without taking into account what is shown in agraphical display.

For example, in the instance where CD3 is coexpressed with a particularantigen, and there is a significant distortion on the population becausethere are many antigens overspilling into the detection channel, theuser may decide not to proceed with such a probe panel. The user thenmay return to any step as seen in FIGS. 2-2D and modify their inputsand/or the default inputs as retrieved from the database.

Detection Limits

One objective in flow cytometry, for example where cells are stainedwith immunofluorescent dyes, is to analyze proteins or markers which areexpressed on the surface of or inside the cell. In this way, it ispossible to categorize cells (or subsets of cells) as being eitherpositive or negative for a particular antigen or marker. Hence, forexample, some cells may be considered “negative” for a particular markeror antigen, and other cells may be considered “positive” for aparticular marker or antigen. In order to determine whether a cell isnegative or positive, it may be helpful to define a threshold of signalintensity, whereby cells which express minimal or nonexistent antigenlevels so as to produce a signal intensity below the threshold areconsidered “negative” and cells which express sufficient antigen levelsso as to produce a signal intensity above the threshold are considered“positive”. Such a threshold of signal intensity may also be referred toas a detection limit.

Hence, in some instances, a detection limit may refer to a thresholdbetween a negative event and a positive event on a particular scale.That is, there may be a positive/negative cut-off, whereby anundetectable or low level of signal or fluorescence corresponds to anegative event. In some cases, unspecific fluorescence may correspond toa negative event. In contrast, a sufficiently high level of signal orfluorescence can be considered as a positive event.

As discussed elsewhere herein, a particular detection limit can be basedon a formulation involving isotype data, fluorescence intensities,distortion factors, and the like.

Isotype Control Staining

A typical immunophenotyping protocol involves labeling cells of abiological sample with antibody-dye conjugates which specifically bindto proteins on the surface of the cells. In this way, proteins expressedby the cells can be analyzed. Due to the nature of the conjugate probeshowever, unwanted non-specific binding may occur between probes andcells. That is, a particular probe may bind to a certain cell, eventhough that probe is not designed to bind to the cell.

Isotype control antibodies can be used to provide a negative control forsuch non-specific binding or fluorescence. Typically, the isotypecontrol is generated from the same host from which the probe antibody isgenerated. For example, if an antibody of a conjugate probe is generatedfrom a mouse, then the isotype antibody used to control for that probeis also generated from a mouse. Moreover, the isotype can be generatedso that is has a specificity for an antigen which is known not to occurin the sample (or that otherwise does not occur on a target cell).

When staining a sample with an isotype control, the resulting signal canbe an indication of non-specific binding of an antibody-dye conjugate toa cell surface. In this way, an isotype control can be used to evaluatethe level of background intensity which may occur in a conjugatestaining procedure. For example, the isotype control can signify thelevel about which fluorescence intensity obtained with a probe can beconsidered to be specific. That is, if when staining with the designedprobe, a detected signal exceeds the background intensity level providedby the isotype control, then it is possible to infer that the detectedsignal corresponds to a positive event.

In practice, an isotype control can be used to distinguish specificbinding from nonspecific binding, to set specificity or gating controls,to designate the location of gates or graphical regions or boundariesused to classify cells, to determine positivity or negativity forparticular antigens, to assign positive/negative boundaries in the data,and the like. Isotype antibody controls can be used in flow cytometryprocedures. It may be particularly helpful to use isotype antibodycontrols when employing multiple stains in a flow cytometry procedure.

In some instances, for example where an antigen has a distinct bimodalexpression, with no overlap between positive and negative population(e.g. CD4 and CD8 on T cells), it may be possible to proceed without useof a control.

Isotype controls can help to address signal to noise issues in a cellanalysis protocol. When determining whether an expression pattern orcell of interest may be positive for a given marker, an isotype controlcan help to optimize the resolution sensitivity without undulysacrificing specificity. When using an isotype control, it is possibleto evaluate whether a detected signal may correspond to only nonspecificbinding, or to some combination of nonspecific and specific binding. Asdiscussed elsewhere herein, isotype controls can be useful fordetermining a detection limit. Isotypes may contribute to background,and spectral overlap or spillover can contribute to background spread,both of which can play a role in sensitivity.

FIG. 4 depicts certain aspects of isotype signaling and resolutionsensitivity. For example, as shown in panel A, the isotype signal on theleft corresponds to a negative event, and the detected signal on theright corresponds to a positive event. Here, the negative population hasa low background relative to the positive population, and thepopulations can be resolved easily (e.g. with a well-defined detectionlimit). In contrast, panel B shows a situation where the negativepopulation has a high background relative to the positive population,and therefore it may be difficult to resolve the populations. Relatedly,panel C shows a situation where the negative population has a lowbackground (relative to the positive population) and a high coefficientof variation (CV), and therefore it may be difficult to resolve thepopulations. Such a situation may be present where an isotype controlpresents significant spillover. Hence, in order to be able to resolvenegative and positive populations, it is helpful to have a negativesignal with low background relative to the positive signal, and to havea negative signal with a low spread or variance.

As discussed elsewhere herein, a dye on one probe can present anemission which spills over into a specific channel intended to detectemission from a dye of another probe. In such cases, the spillover maybe analogous to the data spread of the negative population as describedabove.

In some cases, the broadness of the population in a secondary channelcan increase due to data spread as caused by spillover from anotherfluorescent label on the same population detected in a primary channel,which can contribute to an increase in a detection limit in thesecondary channel. For example, as the measurement error for a negativepopulation increases, the distribution broadness can also increase. Insome cases, there may be a large measurement error, and a detectedsignal in a secondary channel in the presence of spillover from aprimary channel may be beyond an isotype control signal in a secondarychannel in the absence of a spillover from a primary channel, yet maystill correspond to a negative event. In some cases, spillover cancontribute to an increase in detection limit. For example, an increasein the amount of spillover into a particular detection channel canoperate to increase the detection limit for that channel. This may beassociated with a larger measurement error which is caused by a moreintense fluorescence detection on that particular channel.

In some cases, an isotype antibody can be used to obtain a baseline. Insome cases, an unstained cell can be used to obtain a baseline. Thegreater the number of wash steps applied after staining during theprocess, the more likely the isotype functionality is to approach thefunctionality of the unstained cell population, with respect to thebackground fluorescence. That is, extensive washing can operate toequalize the background, such that even though isotype stain has beenadded, the washing acts to remove all of the nonspecifically boundisotype.

Following a staining protocol with a multicolor probe panel, there maybe various fluorochromes associated with the cell surface. In order toassess specific positivity for an antigen as detected by a singlecertain conjugate on those cells, it may be helpful to take into accountthe emission of all other fluorochromes on those cells, which canoperate to increase the detection limit for that particular singlefluorochrome.

Fluorescence Intensity

Typically, flow cytometry involves the use of dyes or fluorochromeshaving certain properties, such as fluorescence intensity or brightnessvalues. For example, the PE dye may have a brightness intensity of3420000, whereas FITC may have a brightness intensity of 39000. Theseintensities can be measured as relative to each other, with one dyebeing chosen as a reference for maximum intensity. As discussedelsewhere herein, the intensity of individual fluorochromes can beconsidered when evaluating probe panels.

Distortion

A distortion of a background population and hence an increase of adetection limit can correspond to the number of overspillingfluorochromes on the cell surface or inside the cell or to the intensityof the related spillover signal into the respective detection channel.Hence, for example, a greater amount of spillover can correspond to abroader background distortion. In some cases, it is possible to considerthe number of antibodies bound to the cell surface, such a greaternumber of antibodies labeled with that particular overspillingfluorochrome are considered to contribute more to the backgrounddistortion. In some instances, a database may contain estimates of thedistortion of the background, for example based on assumed intensitiesthat are typical for a certain cell type. In other words, the distortioncan be based on the number of antibodies labeled with overspillingfluorochromes that are bound to this cell type.

As an example, it is possible to consider a T cell that expresses CD3antigen along with some other coexpressed antigen Z. Typically, T cellshave a very strong expression of CD3 on the surface. Hence, whenstaining with an anti-CD3 probe which is intended to provide emissionsignal to a particular channel, it is possible that the CD3 antibody dyeconjugate probe in fact cross talks to the channel which is intended todetect for the Z probe, then the detection limit for the Z probe may beincreased. When carrying out a multicolor experiment, complex spilloverpatterns may occur. For example, for a ten color experiment, it may benecessary to take into account spillover from nine other colors whenconsidering one particular detection channel. That is, each of the othernine colors may contribute to an increase of the detection limit forthat particular channel. In some cases, the distortion may be dependenton the cell type and the related expression patterns being analyzed. Insome cases, the distortion may be dependent on the number of antibodiesbound on the cell type which carry an overspilling fluorochrome.

Returning to the example of the anti-CD3 probe, it is possible toconsider a T cell (high antigen density of CD3) and a B cell (absence ofCD3 antigen), where both the T cell and B cell express a Z antigen. Whenstaining a T cell with the CD3 probe, that dye can provide a spilloverinto the Z channel detector, thus increasing the detection limit of theZ channel. In contrast, when staining a B cell with that same CD3 probe,there is no (specific) binding because CD3 is absent on the B cell, andhence there is no spillover from the CD3 probe dye, and the CD3 probe isnot considered to cause an increase in the detection limit on the Zchannel. Hence, it can be seen that distortion factors may varydepending on the cell type and related antigen expression patterns. Forexample, a particular probe channel may result in one set of detectionlimits for one type of cell, and another set of detection limits foranother type of cell. Relatedly, the set of detection limits for aparticular probe channel may depend upon what types of antigens areexpressed on the cells being analyzed.

For example, where a cell type is free of antigen A (and hence nobinding by a probe specific for that antigen), an increase in detectionlimit may not be observed at a detection channel which is specific forantigen B. In contrast, where a cell type abundantly expresses antigen A(which is bound by a probe for antigen A, that also provides spilloverto the detection channel for antigen B), then there may be an increasein the detection limit at the detection channel which is specific forantigen B. In this way, the increase in detection limit (or lack ofincrease) can be based on the expression pattern of the stained cells.

The distortion factor thus can have an impact on the detection limit.For example, where there is an increase in the detection limit (e.g. aone decade increase due to spillover), it may be necessary to observe ordetect a greater signal at that channel in order to conclude that thereis a positive event.

For example, with reference to FIG. 8, it can be seen that there isstrong spillover from the PE dye (of the CD4-PE probe) to the FL3channel which detects ECD. Because of the spillover, the uncompensatedECD signal of the specifically PE-labeled population is a few hundredtimes brighter than the uncompensated ECD signal of the non-specificallyPE-labeled background population, as compared to a nonspecific isotypestain.

In some cases, with an increase in the signal, there is an increase inthe spreading of the CD4+ population. Hence, there may be a larger PEsignal on this population. Accordingly, for the FL3 label (y-axis) theremay be an increase in the detection limit.

Compensation

Typically, a higher fluorescence signal may be accompanied by a higherabsolute measurement error. For example, with reference to FIG. 5, thePE positive population causes a signal in the FL3 detection channel forECD, due to spillover of the PE dye emission into the FL3 ECD detectionchannel, and hence the absolute errors may be much higher for thispopulation. This can be represented as a standard deviation. In someinstances, measurement errors may be affected according to Poissondistribution characteristics and cannot be reduced by compensationprocedures, as these can only correct for the mean intensity of apopulation specifically labeled with overspilling conjugates versus apopulation non-specifically labeled with overspilling conjugates. If acomparison of relative measurement errors (as represented by respectivecoefficients of variation CV) indicates a sufficient degree ofsimilarity, this can be a sign of linearity of detection.

In some instances, there may be a factor of two between coefficients ofvariation, such that the CVs are relatively similar and of samemagnitude, and yet the standard deviation may be greater than 100 timesdifferent, or more.

As depicted in FIGS. 6A and 6B, negative fluorescence values, for theexpression signal events measured by an PE detection PMT and by a ECDdetection PMT, plotted against each other, can be artificially generatedin a bimodal distribution through computational compensation. Thebimodal calculation as shown can operate to distinguish events that arenegative for the first of the two measured antibody-dye conjugates, thesecond of the two measured antibody-dye conjugate, or both of the twomeasured antibody-dye conjugates. FIG. 7 shows an increase indistribution broadness for the expression signal events measured by anECD detection PMT and by a PC5 detection PMT, plotted against eachother. FIG. 7, in part, reflects the fact that coexpression can affectthe spread of a negative population into a spillover channel Althoughnot shown in these figures, the spread for the positive events is equalto the spread for the artificially negative events.

As shown in FIG. 8, there may be an increase in detection limit due tostrong spillover. In some instances, calculations implemented viasoftware and/or hardware can operate to correct for spillover, in acompensation procedure. At a certain point of compensation, the PEnegative and the PE positive may have the same mean on the ECD axis,which may refer to a correct compensation (see, e.g. FIG. 5). Asdepicted in FIG. 5, the compensated PE positive population may have alarger measurement error, such that there is a greater data spread ascompared to the PE negative population. Hence, there may be a certainrange of ECD signal that is less intense than the increase ofmeasurement error. Such signals may no longer be detectable as positiveevents. In some cases, there may also be a bifurcation of thecompensated population.

When transforming a certain portion of the Y scale to linear scaling(see, e.g. FIG. 7), it is possible to compress the lower end of thelogarithmic scale. Such transformation can indicate that the measurementerror (e.g. broadness, or spread) of the ECD positive population isgreater than that of the ECD negative population. In some cases,spillover from an FL3 (ECD) positive population to an FL4 (PC5)detection channel can translate to a larger measurement error aftercomputational correction of mean intensities referred to ascompensation. In some instances where there are cell-bound fluorophoresthat have spillover to an FL4 (PC5) detection channel, such dyes as ECDand PE that may be attached to antibodies which detect cellularstructures on the same cells resulting in FL3 (ECD) and FL2 (PE)double-positive cells, there may be superposed spillover to the FL4channel. This may translate to a superposed measurement error. It may bepossible to account for such error. For example, each of thecontributions to the overall data spread can be estimated as a linearvalue, and the linear values can be summed. The result may then betransformed to log value again. Further, a database may containinformation regarding which antigens may be expressed on the same cellsurface. Such information can be used for a basis for the selection ofwhich of the spillover signals are to be combined for this populationthat is FL3 positive. Hence, there may be dyes that also have aspillover into the FL4 channel such as PE, however the antigensassociated with such dyes may not be expressed on the same cell typethat is FL3+, and thus do not contribute to the overall measurementerror in this channel.

According to some embodiments, the cell type, or expression pattern orprofile may have an impact on the results. For example, the results maydepend on the pattern of antigens which is expressed on the cell, andwhich is recognized by the antibody dye conjugate probes. Because ofpotential spillover of some dyes into secondary channels, the detectionlimits for each of the fluorochromes may be influenced. In someinstances where there is spillover, the population in the negative rangeof the scale may be spread or distorted, and the detection limits mayincrease. Typically, a particular expression pattern will be unique to acell type. For example, it is unlikely that two different cell typeswould have identical expression patterns. In order to provide anestimation on the increase in a detection limit, it may be useful toidentify which fluorescence signals will be present on the cells.Accordingly, techniques as discussed herein encompass identifying anexpression pattern for use in evaluating probe panels.

The graph in FIG. 8 depicts results from a staining protocol using anantibody specific for CD4 antigen, conjugated with a PE dye. As shownhere, the FL3 detection channel (sensitive to ECD dye emission) isregistering spillover and hence background distortion from the PEfluorochrome.

As an example, it is helpful to consider a T helper cell which ispositive for CD3 and CD4. In some cases, it may be desirable to evaluatethe cell to see if a cytokine receptor such as CD25 (IL-2 receptor) orCD184 (CXCR4) is present. Often, a cytokine receptor will have a lowcopy number, and thus be dimly staining, on the surface of T helpercell. Hence, where it is desirable to assess cytokine receptor stainingon the FL3 (ECD) channel, it may not be desirable to assign a T helpercell marker (e.g. CD4) on the PE channel. That is because the CD4+ cellswhich are stained with the PE label will present an increase in thedetection limit at FL3 (ECD) where the cytokine detection is desired.

As depicted in FIG. 8, there is an increase in the detection limit,associated with the difference between the detection limit for the CD4−population and the CD4+ population. The line at the lower part of they-axis delimits the CD population by limiting the negative population.This particular graph depicts a significant amount of spillover andhence background distortion from the PE emission spectrum to the ECDdetection channel. Hence, the increase in detection limit isconsiderable. Comparing this result to the negative population on theleft side of the graph, it can be seen that a substantial amount ofsensitivity (e.g. about half of a decade) is lost. Accordingly, thenumber of copies that would be detected with the ECD label would need tobe multiple times (e.g. 6×) higher (e.g. brighter signal) on a CD4+population, as compared with a CD4− population, in order to be detectedor registered as a positive event.

In a related example, it is useful to consider a different detectionchannel, such as the FL5 channel which is intended to detect emissionspectra from the PC7 dye. In this example, the FL5 channel detects acertain wavelength that is more distant to the emission of the PEfluorochrome. Hence, there will be less spillover of the PE emissionspectra into the FL5 channel, and thus the slope of the hinged line(e.g. as shown in FIG. 8) would be less steep. Put another way, thelower the amount of spillover into a particular channel, the lower theslope of the hinged line (e.g as shown in FIG. 8) associated with thatchannel.

The particular wavelength value is another parameter which may impactthe result. Often, the wavelength range associated with a particular PMTor detector is at least in part determined by a bandpass filter that isin front of the filter. Typically, the larger the wavelength beingdetected by a particular PMT, the less sensitive the PMT will be, andhence the PMT will have a larger intrinsic error. For example, a PC7detection channel may be configured to detect light larger than 755 nmwavelength, whereas a PE detection channel may be configured to detectlight at a 575+/−15 nm wavelength. Accordingly, there may be moredistortion associated with the PC7 channel. These effects can befactored into the database or table. Another factor which may beconsidered involves the amount of light spilling over into a secondarychannel. For example, it may be useful to take into account the amountof light a particular dye would typically spillover over into anothersecondary channel. The properties of the secondary channel, with respectto intrinsic measurement error, which may depend on wavelength, can beconsidered.

As depicted in FIG. 8, the distortion factor is provided by the slope ofthe hinged line (detection limit). The distortion factor can be based onan intensity parameter (e.g. intensity of the PE dye signal). Further, alarger wavelength on the secondary channel on the y-axis can correspondto a larger slope. Hence, the distortion factor may increase with thewavelength. In some cases, this can be applied to the intensity of thePE signal. For example, the distortion factor of PE spillover to an ECDchannel may be different from the distortion factor of PE spillover toan infrared channel, in that the distortion factor for the infraredchannel will be larger. Relatedly, because the infrared channel may beat a greater spectral distance from the PE channel, the amount ofspillover light may be less. Typically, PMT detectors can provide a goodrange of linearity, and hence a linear approach is useful. The overalldistortion factor can be approximate to or correlate with the increasein the detection limit. As shown in FIG. 8, the distortion factor can bethe slope of the hinged line the quadrant, and hence the detection limitcan be determined in a linear fashion.

In one embodiment of a detection limit calculation, it is possible toobtain (i) the X value on the X-axis and (ii) the distortion factor orslope, and thereby generate the Y value or detection limit.

For instance, it is possible to provide the relative expression densityFL(x) of a signal that would spillover (e.g. based on CD4 antigendensity), and this value may be obtained from empirical data. It is alsopossible to provide the relative expression fluorochrome intensityFL(x), for example of the PE label, corresponding to the brightness ofthe fluorochrome.

An exemplary empirical approach may involve staining a T cell populationwith a CD4-PE probe, and evaluating the positive population (e.g.separated by 2.5 decades from a negative population). A table of resultscan be generated based on the data.

Hence, by inputting an X value (e.g. an estimate according to a typicalexpression density and dye brightness) along with a slope or linearrelation, it is possible to determine the Y value or detection limit.Considering a probe panel that includes multiple antibody-dyeconjugates, it is possible to obtain a distortion factor for each of thelabels that provide a spillover to the FL3 channel. Results may varyaccording to the dye selected. For example, a CD4-PE probe may provide agreater degree of separation, and a CD4-FITC probe may provide a lesserdegree of separation.

In some instances, the values may be linearized, added, and the sum ofthe linearized values subsequently transformed to a log value, so as toobtain a log distance. For each of the dyes, different distortionfactors may apply. What is more, there may be different signal/noiseratios between positive and negative populations.

According to some embodiments, for a given conjugate combination, for apopulation of CD-Y, where CD-Y is a specific antigen of interest beingmeasured by a single channel, an estimate of the “untouched”signal-to-noise ratio (S/N) distances can be given by:

FL(y)=relative expression density FL(y)*relative fluorochrome intensityFL(y)

where in the case of a modulated marker, the S/N distance FL(y) can beset to zero (0). For the given conjugate combination, an estimate ofspillover from one or more populations of CD-X causing distortion intothe channel measuring for CD-Y for each individual CD-X population canbe given by:

Distortion FL(x)→FL(y)=Distortion factor FL(x)→FL(y)*rel. expressiondensity FL(x)*rel. fluorochrome intensity FL(x)

where in the case that CD-X is a subpopulation of CD-Y, or CD-Y is anexcluding marker set for CD-X, the S/N distortion of FL(x) into FL(y)can be set to zero (0). The distortions from each channel in a systemhaving a plurality of channels (for example, 10 total channels and thus9 CD-X channels) can be accumulated as given by:

FL(x)→FL(y)_tot=log Σ10{circumflex over ( )}(FL(x;1 . . . 9)→FL(y))

The set of estimates and aggregation can be performed for each channelin a system, for each antigen of interest individually, allowing for thecalculation of an effective S/N distance for each antigen as given by:

Effective S/N distance=FL(y)−FL(x)→FL(y)_tot

where the effective S/N distance for each antigen in each channel can beused to further determine detection limits for the overall system andpanel.

According to some embodiments, the detection limit (DL) for the specificpositivity of a fluorescent member of CD-Y, represented by FL(y), is anear-to linear function of positive spillover from fluorescence from amember of CD-X, represented by FL(x) according to the equations:

DL(FL(y))=DLIC(FL(y))+256*(DF(FL(x)→FL(y))*RI(FL(x)))

where DLIC(FL(y)) is defined as the detection limit of the fluorescenceof CD-Y for an isotype control staining stated as coordinates between 0and 1023; where RI(FL(x)) defined as the intensity of the fluorescenceof a CD-X above its DLIC(FL(x)) stated in decades of FL(x); and whereDF(FL(x)→FL(y) is defined as the distortion factor, measured as theincrease of DL(FL(y)) per RI(FL(x)), stated in decades FL(y) divided bydecades FL(x).

In some aspects, DF(FL(x)→FL(y)) can be calculated from thepositive/negative compensation procedures and correlated according tothe equation:

DF(FL(x)→FL(y))=(FL(y,x)*FLIC(x))/(FL(x)*FLIC(y))

where FL(y,x) is defined as the FL(y) intensity of FL(x) positiveevents, and where FLIC(x) and FLIC(y) are defined as the intensity ofisotype control stainings for their respective antigens. Theaccumulation effects of overspilling compensated intensities from CD-Xmembers, FL(x1, x2, . . . x(n−1)) into CD-Y can be given by:

DL(FL(y))total=DLIC(FL(y))+256 log Σ10{circumflex over( )}((DF(FL(x1,x2, . . . x(n−1))→FL(y)*RI(FL(x1,x2, . . . x(n−1)))))

Such calculations are also described in the second paragraph of FIG. 1D.

According to some embodiments, isotype control data can be provided perpatient gated on a scatter or “silently” stained (leukocyte) population.In other embodiments, compensation data can be generated per application(or per panel) by a positive/negative algorithm, where a mathematical oran experimental procedure for the calculation of a distortion factor canbe established. In further embodiments, distortion matrix can begeneration based on either or both of isotype and compensated data.Following compensation, a distortion matrix can be applied on everysingle event. In some embodiments, knowledge of compensation factors maynot be sufficient to complete calculations as described herein. In otherembodiments, knowledge of a distortion matrix, which can be determinedexperimentally on a given instrument or an instrument configuration anda set of dye, may be sufficient to complete calculations as describedherein. In yet further embodiments, specifically positive gated valuescan be displayed in a way such as prism, a tree, a three dimensionaloverlay, a comparison plot, a profile plot, or the like.

With reference to FIG. 8, it can be seen that techniques can involveinputting or selecting a value for the X-axis, corresponding toexpression density multiplied by the fluorescence intensity. Such datacan be implemented in an antibody table module (and read or retrievedtherefrom) as discussed elsewhere herein. In some cases, the values maybe estimated. In some cases, the values can be based on experimentaldata. For example, it is possible to analyze CD4-PE staining experimentsto obtain such data. Further, methods may involve multiplying thedistortion factor by the intensity (real or estimated) of the marker. Insome instances, the expression densities or marker patterns (e.g. fortarget cells) can have default parameters or values. Such informationcan be implemented in an antibody database module. As describedelsewhere herein, the second paragraph of FIG. 1D provides additionaldetails for exemplary detection limit calculations.

As depicted in FIG. 9, an increase of detection limits can beindependent of compensation factors and/or PMT voltages. Rather, thedetection limit and/or increase thereof can depend on emission spectraand filter configuration. As illustrated, configurations for how resultsare depicted can be tailored to remove distortion (or in other words, toapply a compensation factor) that may be caused by sensor readings froma desired PMT being incorrectly measured in combination with sensorreadings from a separate PMT channel. Specifically, FIG. 9 displaysdetection results, sought from an ECD channel, with a percentage ofsensor readings from a PE channel subtracted from the detection results.Three exemplary variations of PE channel subtraction are shown: one with34% of the PE channel signal subtracted, one with 46% of the PE channelsignal subtracted, and one with 62% of the PE channel signal subtracted.In each plot of detection limit results, the number of events identifiedas positive or negative changes, but the detection hinge line betweenthe two compared populations remains the same. The removal of distortionprovides for a more accurate and sensitive result set.

FIG. 10 shows aspects of an exemplary spillover pattern distortionmatrix for certain dyes, according to some embodiments. The spillovermay be independent of PMT settings and compensation factors, and may bedependent on fluorochromes, filters, and precision of alignment. Inaspects as illustrated, the spillover matrix can be qualitative, toquickly display to an operator the effect at the interface of particulardyes or PMT detectors.

FIG. 11 shows aspects of a coexpression matrix according to someembodiments. In aspects as illustrated, the coexpression matrix can bequalitative, to quickly display to an operator the effect at theinterface of particular antigens, dyes, or PMT detectors. As illustratedin FIG. 11, a coexpression matrix can indicate interactions where: acolumn is non-exclusively expressed with a row, a column is an exclusivesub-population of a row, or where a column is mutually exclusive of arow (providing for a symmetrical plot of expression events).

FIG. 12A depicts estimates of schematic staining patterns according tosome embodiments. Where there is positivity for a certain markeroverspilling to another channel, a hinge may be observed, as shown inthe right graph. The user may change certain inputs into a simulation,so as to vary the output shown. For example, a user may select or changevarious dyes or antibodies contained in a probe panel, so as to obtainan improved sensitivity for a certain coexpressed antigen. The situationshown in the middle graph of FIG. 12A is particularly desirable, as thedetection limit of the FL(x) positive population is not influenced bythe FL(x) positivity, as compared to the negative.

FIG. 12B depicts a variable PE detection limit signal according to someembodiments. The PE signal of FIG. 12B can correspond to the horizontaldirection depicted in the graph of FIG. 12C. That is, the spillover thatis manifested in FIG. 12B can also be manifested in FIG. 12C resultingin a variable PE detection limit that depends on the fluorescenceintensity of the overspilling dye whose intensity is scaled along they-axis. In comparison, in FIG. 12D the overspilling antigen has beenmoved to a position where it exerts no spillover to the PE channel (e.g.CD45 moved from ECD excited the blue laser to APC excited by the redlaser with a very strong signal), such that the CD45-APC signal does notimpact the background at the PE channel. Further, the PE and APCfluorophores are excited by different laser wavelengths. Suchnon-spillover situations may be rare, particularly in multi-color (e.g.10) panel configurations, because typically there will be fluorescentlabels on the cells which influence detection limits at variouschannels.

In FIG. 12E, there is a high sensitivity for the expression of antigen Yon cells that are positive for antigen X. This may be a desirablesituation where the expression of Y is prioritized. When comparing thenegative and positive populations, the detection limit is not altered.There is a low detection limit for FLy, on the FLx population. Accordingto some embodiments, FIG. 12E may refer to a special situation involvinghigh expressing cells of a non-exclusive co-expressed antigen that areat risk to be specifically detected. In some cases, it may be helpful torefer to an uncommented doublet of FIG. 12C.

In comparison, in FIG. 12F it can be seen that the detection of antigenX labeling on the cell involves a fluorochrome emission that influencesthe detection limit on FLy. An overlap of populations is also shown. Incontrast, there is no such overlap in FIG. 12E (or an uncommenteddoublet of FIG. 12C). Hence, a user that obtains results such as thosedepicted in FIG. 12E may decide to proceed with that particular probepanel. In contrast, if this coexpression is prioritized, a userobtaining results such as those depicted in FIG. 12F may decide not toproceed with that particular probe panel.

FIG. 12G (as well as an uncommented doublet of FIG. 12C) depicts abivariate dot plot showing a double positive event. FIG. 12H depicts asimilar bivariate dot plot, where there is no double positive event. Theplot of FIG. 12H is representative of a result for an exclusion.

FIG. 12I depicts another exclusion situation, where there are no doublepositive events, which may present an acceptable result for the user.Considering the negative for Antigen 1, which excludes Antigen 2, it canbe seen that these two antigens do not occur on the same cell type.Hence, a user that is interested in evaluating for Antigen 2 on a cellcharacterized by the lower left quadrant can observe an unbiaseddetection limit. The user would not look to a cell expressing Antigen 2,because due to the biological characteristic of the cell, a doublepositive population does not occur. Hence, a double positive may not bea target population in the analysis. The discrimination between theAntigen 2 positive (AG2-POS) and the Antigen 2 negative (AG2-NEG) can,however, be a target of the analysis. Similarly, the discriminationbetween the Antigen 1 positive (AG1-POS) and the Antigen 1 negative(AG1-NEG) can also be a target of the analysis. In each of thesesituations, the detection limits are unchanged.

Because there is no expression of Y antigen on X positive cells, nor anyexpression of X antigen on Y positive cells, the upper right quadrant isnot populated.

FIG. 13 depicts aspects of probe panel evaluation systems and methodsaccording to some embodiments. In some cases, expression patterns can becategorized based on various criteria. For example, in some embodiments,expression patterns can be categorized as normal, lymphoproliferative,or immature blood cell disorders. As shown here, various factors can betaken into account for expression patterns, such as fluorescentintensity and the like. In some cases, the cells in the upper table ofFIG. 13 identifying expression characteristics can be annotated with a“1” to represent marker coexpression, and with a “0” to represent theabsence of marker coexpression. In some cases, the cells in the lowertable of FIG. 13, also identifying expression characteristics, can beannotated with a “0” to represent descendant-to-parent markercoexpression, and with a “1” to represent the absence ofdescendant-to-parent marker coexpression. Accordingly, this data can beused to indicate whether a fluorochrome label may or may not result inan increase of detection limit for a secondary fluorophore label. Forexample, where there is no coexpression, then it may be possible to nottake into account the fluorescent label on the cell. That is, there willbe no production of a spillover signal, due to the absence on the samecell, and hence there will be no increase in detection limit. Asdiscussed elsewhere herein, embodiments of the present disclosureencompass aspects of other expression patterns, such asparent-descendent patterns. Expression pattern information can be usedto determine whether a measurement error at a certain channel for acertain population with a certain expression pattern will effectively beincreased or not. Relatedly, expression pattern information can be usedto estimate or determine distortion, and/or for superpositioning. Insome cases, exemplary tables account for spillover that occurs so as toprovide a combined distortion result.

FIG. 14 depicts aspects of probe panel evaluation techniques,particularly numeric simulation techniques, according to someembodiments. The tables provided as FIG. 14 display, in part, therelative contributions of various fluorochromes, exited by one or moreexcitation lasers and detected in FL-channels (i.e. PMT channels). Therelative contributions provide a basis for correction, the removal ofparameters or events undesirably measured by a given PMT relating tofluorochromes to be measured by another PMT detector and channel. Aneffective distortion matrix can be calculated to determine such relativecontributions for any given combination of excitation lasers,fluorochromes and PMT detectors.

FIG. 15 depicts aspects of probe panel evaluation techniques,particularly spillover pattern techniques, according to someembodiments. The tables provided as FIG. 15 display, in part, therelative brightness of the given fluorochromes in relation to a singlefluorochrome of the set, chose as the reference for 100% brightness orintensity in that panel of dyes. The spillover pattern and relativebrightness can be further classified as creating a particular level ofdistortion, indicating the relevance of a particular fluorochrome memberin affecting the detection of other fluorochromes by related PMTdetectors. These values can be used to calculate an increase ofdetection limits, per decade of distorting signal intensity, for eachgiven combination of channel or dye.

FIG. 16A illustrates an exemplary schema for a probe panel systemaccording to some embodiments. As depicted here, the system includes asimulator graphics module, a simulator numeric module, an spilloverpattern module, and an antibody database module. The simulator graphicsmodule can be used for user input of aspects of a desired antibodypanel, for the graphical output of simulation results, and for themodification of default simulation parameters. The simulator numericmodule can be used for the numerical output of simulation results. Thespillover patterns module can be used for default simulation parametersfor fluorochrome properties and distortion factors according to standardoptical filter sets. The antibody database module can be used fordefault simulation parameters for antigen expression densities,coexpression patterns, and parent-descendant schemes.

In some cases, for example as shown in FIG. 13, the simulator graphicsmodule can be configured to implement user input related to an antibodypanel, the display of default data on the coexpression of antigens (e.g.“may be coexpressed with/coexpression may be of interest”), the displayof default data on subpopulation coexpression of (e.g. “(brightexpressing) cells are not an (exclusive) subpopulation of”), the displayof estimated data on conjugates' decades of mean signal intensity abovepositive-negative threshold, and the graphical output of simulationresults.

FIGS. 16B and 16C depict aspects of a user input module for a probepanel according to some embodiments. Following user input of antigenspecificity the respective part numbers (PN) can be retrieved from theAntibody Database module and displayed in the fields below the userinput fields. For unoccupied channels, the user may input a dummydesignation. Where antibody-conjugates are not available in therepertoire of the Antibody Database, and where the antigen specificityis available albeit conjugated to dye labels other than those desired,it is possible to designate such as Customer Design Service (CDS).

FIG. 16D depicts aspects of a simulator graphic module according to someembodiments. As shown here, the system may provide a display of defaultdata on the coexpression of antigens (e.g. “may be coexpressedwith/coexpression may be of interest”). In some cases, the source range(e.g. normal, lymphoproliferative, or leukemic disease, corresponding toexpression pattern, can be switched or selected by entering 0, 1 or 2).The specificities as provided by a user can be displayed, for example ascolumn and row headers. In some embodiments, the system can beconfigured to receive input from the user so as to override defaultdata. In some embodiments, table entries can be overwritten by a user ifneeded or desired. In some cases, coexpression data (e.g. defaultentries) can be retrieved from the Antibody Database module. Asindicated here, a value of “1” can represent a True case, such thatcoexpression occurs and is of interest. Relatedly, a value of “0” canrepresent a False case, such that antigens are not coexpressed, or theircoexpression is not of interest. Such coexpression data can be stored inthe Antibody Database module and retrieved therefrom. There is symmetryalong the diagonal axis of the blank fields.

FIG. 16E depicts aspects of a simulator graphic module according to someembodiments. As shown here, the system may provide a display of defaultdata on the parent-descendant relationships of antigens (e.g. “(brightexpressing) cells are not a descendant population of”). Again, a sourcerange can be switched by entering 0, 1 or 2. As further shown here,antigen specificities according to user input can be displayed as columnand row headers in the table. In some cases, entries may be overwrittenby a user if needed or desired. In some cases, parent-descendant data(e.g. default) can be retrieved from an Antibody Database module. Avalue of “1” can represent a true case, where positive cells (columnantigen) are not a descendant population, and therefore cause distortionin channel of row antigen. A value of “0” can represent a false case,where positive cells (column antigen) are a descendant population, andtherefore do not cause distortion in channel of row antigen. Forexample, according to the table shown here, lambda is not a descendentof kappa, whereas FMC7 is a descendent of CD19. According to someembodiments, FMC7 may also be kappa negative.

FIG. 16F depicts aspects of a simulator graphic module according to someembodiments. As shown here, different curves or lines can correspond todifferent excitation wavelengths. Here, the wavelengths are 488 nm, 638nm, and 405 nm. The circles, connected by solid lines, correspond to thebackground for most complex expression patterns. The squares, connectedby dashed lines, correspond to the lowest expected fluorescenceintensity for a conjugate. The triangles, connected by dotted lines,correspond to the brightest expected fluorescence intensity for aconjugate. Each individual indicator (i.e. circle, square, or triangle)is positioned above an X-axis value corresponding to a centralwavelength for a particular bandpass filter or detection channel range.The Y-axis represents upper four log decades of fluorescent intensity,with a negative population centered in the lowest decade. As discussedelsewhere herein, for an antigen having discrete expressioncharacteristics, the dashed and dotted lines will coincide for arespective X-axis location (bandpass wavelength). Further, as discussedelsewhere herein, it is possible to rank various probe panel designsbased on distances between various indicators. For example, probe paneldesigns can be ranked based on a maximum distance between a triangle(dotted line) and a circle (solid line) and can also be based on aminimum distance between a square (dashed line) and a circle (solidline).

In some cases, ranking may involve prioritizing probe panels thatcorrespond to complex spillover patterns and which are associated withdimly expressed antigens.

FIG. 16G depicts aspects of a simulator graphic module according to someembodiments. Here, a display of all distortion contributions for aparticular detection channel are positioned above each individualchannel. The circles or bubbles can be coded (e.g. color coded)according to the respective overspilling fluorochrome. The value orintercept on the Y-axis indicates an absolute increase of detectionlimit in decades caused by a conjugate in a respective channel. Forexample, the Y-axis can represent an increase of background, in decades,where 0.00 is a threshold between the first and second decades (e.g.negative population centered in first decade). The diameter of thecircle can represent the relative contribution of a conjugate to theoverall background distortion in a respective channel. As discussedelsewhere herein, in order to lower the background distortion for agiven channel, it may be helpful to address for example the largestcircle positioned above that particular channel. In some cases, it maybe useful to minimize or reduce the number of circles for channels usedfor the detection of modulated antigens.

FIG. 16H depicts aspects of a simulator numerics module according tosome embodiments. As shown here, the table includes values for allabsolute distortions resolved per overspilling conjugate (column titles)and distorted channel, and also illustrates an increase in backgroundsin decades per decade of signal intensity of the spilloveringfluorochrome in its primary channel. Further included are columns forthe size of maximal contributions to total background distortion andtotal distortions of the background. The superposed column indicates aquadrant or region position in a graphical scale of 1024 units of apositive-negative threshold according to a position parameter. Alsoshown is source data for the graphical representation of backgrounddistortion.

According to some embodiments, it is possible to determine an absolutedistortion caused by a single spillovering conjugate based on thefollowing formula:

(conjugate intensity)*(distortion factor)*(coexpressionindex)*(parent-descendent index)

According to some embodiments, the conjugate intensity may berepresented in decades, and can be determined as an estimate based onantigen density and fluorochrome brightness or by usage of empiricaldata on conjugate intensity.

According to some embodiments, it is possible to determine a totaldistortion in a given channel based on the following formula:

LOG₁₀(sum of linearized absolute distortions caused by each conjugate)

According to some embodiments, a positive-negative thresholdregion/quadrant position can be determined based on the followingformula:

(decades of total distortion)*(256)+256

where 256 is the graphical threshold between the first and the seconddecade assuming that the negative population is delineated by thisgraphical threshold.

FIG. 16I depicts aspects of a simulator numerics module according tosome embodiments. The table shown here includes linear relativedistortion contributions resolved per crosstalking conjugate (columntitles) and the distorted channel in decades. This table can providesource data for the graphical representation of background distortion(e.g. graphics, “distortion contributions”) Table values can be based onthe following formula:

${{relative}\mspace{14mu} {contributions}\mspace{14mu} {to}\mspace{14mu} {distortion}} = {\frac{\left\lbrack {{linear}\mspace{14mu} {absolute}\mspace{14mu} {distortion}} \right\rbrack}{\left\lbrack {{linear}\mspace{14mu} {total}\mspace{14mu} {distortion}} \right\rbrack}.}$

FIG. 16J depicts aspects of an spillover pattern module according tosome embodiments. The table shown here includes distortion factors,which can be applied per decade of crosstalking (i.e. overspilling)fluorescence intensity and can be determined experimentally or based onthe following approximative equation:

distortion factor=[crosstalk index]*[bandpass temperature factor]

FIG. 16K depicts aspects of an spillover pattern module according tosome embodiments. A definition of crosstalk index can be seen herein as:

Crosstalk index=LOG(SNR(secondary signal)/LOG(SNR(primary signal);

-   -   where: SNR=signal-to-noise ratio=MFI (positive population)/MFI        (negative population); and with MFI=mean fluorescence intensity.

FIG. 16L depicts aspects of an spillover pattern module according tosome embodiments. As shown here, for 525 nm (FITC) the absolutedistortion can be calculated according to the following formula wherethe ratio of DF (distortion factor) to CI (crosstalk index) is 0.15:

absolute distortion=0.15×LOG(SNR(secondary))

That is, there are 0.15 decades of distortion per decade of secondarysignal intensity. As also shown here, for 660 nm (APC) the absolutiondistortion can be calculated according to the following formula wherethe ratio of DF to CI is 0.42:

absolute distortion=0.42×LOG(SNR(secondary)

That is, there are 0.42 decades of distortion per decade of secondarysignal intensity in the APC channel (660/20 bandpass).

FIGS. 16M and 16N depict aspects of an antibody database moduleaccording to some embodiments. The table in FIG. 16M includes data forantigen expression densities (e.g. scaled according to CD8-PE=2.5decades of intensity above pos-neg threshold), part numbers, andexpression characteristics (e.g. 0=modulated, 1=discrete). The table inFIG. 16N includes data for coexpression patterns (e.g. “may becoexpressed with/coexpression may be of interest”). As shown here, thereis a symmetry in the table along the diagonal table cells.

FIG. 16O depicts aspects of an antibody database module according tosome embodiments. In some embodiments, antibody database modules caninclude information concerning parent-descendant patterns (e.g. “(brightexpressing) cells are not a descendant population of”), and may beasymmetric. According to some embodiments, such database information caninclude estimated values. According to some embodiments, such databaseinformation can include empirical values.

FIG. 17 depicts aspects of a numerical approach to model spilloverpatterns, according to some embodiments. As shown here, the evaluationof spillover patterns may involve a detection radar approach to processa multivariate data set. As shown in FIG. 17, the multivariate radarrepresentation can display detection limits for antigens according totheir detection channels as they are arranged in their panel setup 1702.As shown in a first image layer 1704, each radial axis of the detectionradar represents a fluorescence channel from the third to sixth decadeof measured signal, each decade being a 20 bit segment. The firstinterior shaded portion 1706 represents untouched detection limits,within and below that decade, assuming that the isotype control iscentered in the third decade. As shown in a second image layer 1708, asecond interior shaded portion 1710 underlies the first interior shadedportion 1706, where the second image layer 1708 represents the estimatedbackground distortion for each fluorescence channel. Values for theestimated distortion generating the second interior shaded portion 1710are based upon the given combination conjugates according to their:spillover pattern, relative fluorochrome intensities, antigen densities,coexpression matrix, and distortion matrix.

A third image layer 1712 further includes a low limit 1714 representingthe low limit of expected fluorescence intensity for each conjugate. Allmodulated or indiscrete expressions are set to zero as part of the lowlimit 1714 of expected fluorescence intensity. A fourth image layer 1716further includes a high limit 1718 representing the high limit ofexpected fluorescence for each conjugate. Discrete expressions of aparticular antigen and dye have equal low limit 1714 and high limit 1718values for expected fluorescence intensity.

FIGS. 18A and 18B depict aspects of a numerical approach to modelspillover patterns, according to some embodiments. As shown here, theevaluation of spillover patterns may involve a detection radar approachand/or a distortion indicator approach. In such distortion indicatorrepresentations, each DL can have a color that represents a distortingdye. The Y-axis can represent the amount of distortion, in decades,while the X-axis can represent each distorted FL channel. The diameterof a data point (i.e. the size of a DL) can represent the relativecontribution of a single conjugate to the overall distortion.

FIGS. 19A and 19B depict aspects of a numerical approach to modelspillover patterns, according to some embodiments. As shown here, theevaluation of spillover patterns may involve a clonality screenapproach, which can further be represented with a multivariate radarapproach.

Upon simulation of a probe panel design as set forth herein, a flowcytometry device can be set up and operated using said probe paneldesign. In particular, a processor or system, which can be anon-transitory computer-readable media, can receive informationregarding a flow cytometer hardware configuration, information regardinga roster comprising a plurality of probes, the individual probes of theroster being associated with respective individual channel-specificdetection limits, and information regarding an antigenic coexpressionpattern. The processor, or one or more additional, informationallylinked processors, can evaluate combinations of individual probes as theprobe panel, based on the flow cytometer hardware configuration, theindividual channel-specific detection limits, and the antigeniccoexpression pattern, the combinations being subsets of probes from theroster, and can further determine the probe panel for use with the flowcytometer hardware configuration, individual channel-specific detectionlimits, and antigenic coexpression pattern. Finally, a probe panel foruse in a flow cytometry procedure can be output, and used by an operatorfor probe panel design for a flow cytometry instrument and experiment.

Interface and Selection of Parameters for Panel Design and Simulation

Determination of a probe panel can be presented in a web-basedinterface, allowing for a user to design a probe panel for a flowcytometry experiment using online databases and tools. Particularembodiments of systems and methods (as described above in FIGS. 2-2D)can be provided to an operator as shown in FIGS. 20A-20F. Aspects of thesystem or method can be provided to an operator as a single form, atransitional form, or as multiple forms representing steps in themethod.

FIG. 20A is an exemplary image of an interface screen that allows forthe selection of a hardware configuration 2000 (also referred to as aHardware Configuration screen 2000). A selection of tabs 2002 allows forthe movement between various fields in which data can be entered, whichas shown are tabs for steps of a method of simulating a panel. The tabtitle field 2004 can indicate which step of a method a user is viewingor editing, which in FIG. 20A is “Hardware Configuration”, indicated as“Step 1” within the selection of tabs 2002. Drop down field 2006 allowsfor an operator to select a hardware configuration providing informationfrom a database to establish parameters for performing a panelsimulation. The hardware configuration graphical display 2008 can showan operator details of a hardware configuration, including but notlimited to, one or more excitation lasers, the wavelengths of excitementof the one or more excitation lasers, voltages or other power values forthe one or more excitation lasers, one or more PMT detectors, voltagesor other power values for the one or more PMT detectors, or bandpassfilters for each of the one or more PMT detectors. In some aspects, theselection of a hardware configuration may be automatically determined bythe database and system. The hardware configuration selected can setparameters and values for a panel design and simulation describedherein. A selectable “Next >” field 2010 is provided for advancing to alater step in the method. A generally selectable “<Back” field 2012 isprovided for advancing to an earlier step in the method, however, in theshown Hardware Configuration screen 2000, there is no earlier step, andthus the “<Back” field 2012 is not selectable on the HardwareConfiguration screen 2000.

FIG. 20B is an exemplary image of an interface that allows for antibodyselection 2014 (also referred to as an Antibody Selection screen 2014).The tab title field 2004 in FIG. 20B is titled “Antibody Selection”, andis indicated as “Step 2” within the selection of tabs 2002. A pluralityof specificity drop down fields 2016 allows for an operator to select aspecificity, and a plurality of dye drop down field 2018 allows for anoperator to select a dye. In some embodiments, the specificity to beselected in the specificity drop down fields 2016 can be a selection ofantigens or antibodies. For each specificity selected in the specificitydrop down fields 2016, a corresponding dye can be selected from theplurality of dye drop down fields 2018. In some aspects, the selectionof specificity drop down fields 2016 and dye drop down fields 2018 maybe automatically determined by the database and system. The antibodiesselected can set parameters and values for a panel design and simulationdescribed herein. A selectable “Next >” field 2010 is provided foradvancing to a later step in the method. A selectable “<Back” field 2012is provided for advancing to an earlier step in the method.

FIG. 20C is an exemplary image of an interface that allows for targetphenotype selection 2020 (also referred to as an Target Phenotypesscreen 2020). The tab title field 2004 in FIG. 20C is titled “TargetPhenotypes”, and is indicated as “Step 3” within the selection of tabs2002. A target population drop down field 2022 allows for an operator toselect a target population relating to specific antigens that do orshould express a target phenotype. A plurality of “Unrelated Antigens”2024 is provided, listing a selection of antibody and dye conjugatesthat are not, or may not be, related to a selected target population.Each antibody and dye pair in the plurality of Unrelated Antigens 2024includes a selectable check-box to indicate that the antibody and dyepair is an antigen of interest. A plurality of “Antigens of Interest”2026 is provided, listing a selection of antibody and dye conjugatesthat are, or are believed to be, related to a selected targetpopulation. The members of the plurality of Antigens of Interest 2026can be populated by antibody and dye pairs originally provided in theplurality of Unrelated Antigens 2024, or may be autopopulated accordingto relationships data stored in the database and system. The indicationthat a particular antibody and dye pair is a member of the plurality ofAntigens of Interest 2026 can be removed by deselecting a selectablecheck box for the antibody and dye pair. In some aspects, the selectionof the plurality of Unrelated Antigens 2024 and the plurality ofAntigens of Interest 2026 may be automatically determined by thedatabase and system. In some aspects, an Antigen of Interest globalselection field 2028 can be provided, having an activatable field toselect all antibody and dye pairs as members of the plurality ofAntigens of Interest 2026 and an activatable field to indicated allantibody and dye pairs as members of the plurality of Unrelated Antigens2024. The target phenotypes, unrelated antigens, and antigens ofinterest selected can set parameters and values for a panel design andsimulation described herein. Although not expressly shown in FIG. 20C, Aselectable “Next >” field is provided for advancing to a later step inthe method and a selectable “<Back” field is provided for advancing toan earlier step in the method.

FIG. 20D is an exemplary image of an interface that allows foridentification of mutually excluding antigens 2030 (also referred to asMutually Excluding Antigen screen 2030). The tab title field 2004 inFIG. 20D is titled “Mutually Excluding Antigens”, and is indicated as“Step 4” within the selection of tabs 2002. A selection of expandableantigen-antibody paring fields 2032 are provided, where when expanded,antigen-antibody paring field provides a listing of antigens to exclude2034; this can be a list of potential antigens that may be mutuallyexcluding with the antigen-antibody paring for an individual field. Thelisting of antigens to exclude 2034 can have a check-box by each listedantigen to select a particular antigen to indicate as excluded, which isan indication that the selected antigen or antigens does not coexpresswith the antigen identified as the part of the antigen-antibody paringfor that field. The listing of antigens to exclude 2034 can furtherinclude one or more selection links that can cause all antigens in thelisting 2034 to be selected, or to de-select all antigens in the listing2034. Antigens that are indicated as excluded can be listed in anidentified excluded antigens list 2036. The listing of identifiedexcluded antigens 2036 can have a selected check-box by each listedantigen, which can be de-selected to indicate that the particularantigen is not mutually excluded for a given antigen-antibody paringfield. In aspects, the listing of antigens to exclude 2034 can beselected by an operator selecting one or more listed antigens based onan operator-generated rationale. In other aspects, the listing ofidentified excluded antigens 2036 can be specified by the database andsystem; a selectable “Auto Specify Exclusions” field 2038 is provided toallow an operator to indicate mutually excluded antigens according todata stored in the database and system. Conversely, a selectable“Discard All Exclusions” field 2040 is provided to allow an operator tode-select all previously indicated mutual exclusions for one or moreantibody-antigen paring fields. The mutually excluding antigens selectedcan set parameters and values for a panel design and simulationdescribed herein. Although not expressly shown in FIG. 20D, A selectable“Next >” field is provided for advancing to a later step in the methodand a selectable “<Back” field is provided for advancing to an earlierstep in the method.

FIG. 20E is an exemplary image of an interface that allows foridentification of parent and descendant antigens 2042 (also referred toas Parent & Descendent Antigens screen 2042). The tab title field 2004in FIG. 20E is titled “Parents & Descendants”, and is indicated as “Step5” within the selection of tabs 2002. A selection of expandableantigen-antibody paring fields 2044 are provided, where when expanded,antigen-antibody paring field provides a listing of descendant antigens2046; this can be a list of descendant antigens, being descendants aparent antigen indicated as the antigen of the antigen-antibody paringfor an individual field. The listing of descendant antigens 2046 canhave a check-box by each listed antigen to select a particular antigento indicate as a descendant, which is an indication that the selectedantigen or antigens is a cluster of differentiation later in adevelopmental progression or step than the cluster of differentiationindicated as the selected antigen of the antigen-antibody paring forthat field. The listing of descendant antigens 2046 can further includeone or more selection links that can cause all antigens in the listing2046 to be selected, or to de-select all antigens in the listing 2046.Antigens that are indicated as descendants can be listed in anidentified descendant antigens list 2048. The listing of identifieddescendant antigens list 2048 can have a selected check-box by eachlisted antigen, which can be de-selected to indicate that the particularantigen is not a descendant for a given antigen-antibody paring field.In aspects, the listing of descendant antigens 2046 can be selected byan operator selecting one or more listed antigens based on anoperator-generated rationale. In other aspects, the listing ofdescendant antigens 2046 can be specified by the database and system; aselectable “Auto Specify Family Patterns” field 2050 is provided toallow an operator to indicate parent and descendant antigenrelationships according to data stored in the database and system.Conversely, a selectable “Discard All Family Patterns” field 2052 isprovided to allow an operator to de-select all previously indicatedparent and descendant antigen relationships for one or moreantibody-antigen paring fields. The parent and descendant antigenrelationships selected can set parameters and values for a panel designand simulation described herein. Although not expressly shown in FIG.20E, A selectable “Next >” field is provided for advancing to a laterstep in the method and a selectable “<Back” field is provided foradvancing to an earlier step in the method.

FIG. 20F is an exemplary image of an interface that allows foridentification of and antigen densities 2052 (also referred to asAntigen Density screen 2052). The tab title field 2004 in FIG. 20F istitled “Antigen Densities”, and is indicated as “Step 6” within theselection of tabs 2002. A target population drop down field 2056 allowsfor an operator to select a target population relating to specificantigens, and particularly the density of the specific antigen for aparticular cell type or target population. The density of antigens in aregion of a cell, or for a type of cell or target population,corresponds to the strength of expression of that antigen, and how itcan interact or be measured with populations of other antigens. Asignal-to-noise display 2058 can indicate across decades of expressionthe ratio of signal expected from a target population and correspondingantigen over background noise. A discrete/modulated selection field 2060can be provided, which in aspects can be a radio button selection field,to configure the signal-to-noise display 2058 to display the expressionof the target population when evaluated according to discrete ormodulated parameters, as described above. Similarly, discriminationselection field 2062 can be provided, which in aspects can be a radiobutton selection field, to configure the signal-to-noise display 2058 todiscriminate the results of the target population as either betweenpositive and negative expression or as between bright positive and dimpositive expression, as described above. A slider setting can be set forthe signal-to-noise display 2058, to adjust the scale and scope of theregion of the signal-to-noise display 2058 displayed. In some aspects,the slider setting can be chosen with a selectable “Auto Set Slider”field 2064, using the data of the antigen and target population displayto automatically set the range of the signal-to-noise display 2058. Inother aspects, an operator can direct the signal-to-noise display 2058to have a specific scale by manually adjusting a slider interface 2066,which can be a radio slider interface. The target populations, discreteversus modulated display selection, discrimination selection, slidersetting, and antigen densities selected can set parameters and valuesfor a panel design and simulation described herein. Although notexpressly shown in FIG. 20F, A selectable “Next >” field is provided foradvancing to a later step in the method and a selectable “<Back” fieldis provided for advancing to an earlier step in the method.

FIG. 20G is an exemplary image of an interface that displays a panelexpression simulation estimate for a given antigen panel design 2068(also referred to as Panel Simulation screen 2068). Information takenfrom the input into the Hardware Configuration screen 2000, AntibodySelection screen 2014, Target Phenotypes screen 2020, Mutually ExcludingAntigen screen 2030, Parent & Descendent Antigens screen 2042, andAntigen Density screen 2052 can be collected and used to calculate anexpected expression or staining pattern. The tab title field 2004 inFIG. 20G is titled “estimated Staining Patterns”, and is indicated as“Step 7” within the selection of tabs 2002. A display field 2070 canprovide either or both of numerical and graphical representations ofestimated antigen expression for a given panel design, including but notlimited to graphical representations discussed herein. The generallyselectable “Next >” field 2010 shown is not selectable on the PanelSimulation screen 2068 because there is no later step in thecorresponding method or evaluation to advance to. The selectable “<Back”field 2012 is provided for advancing to an earlier step in the method.

In some embodiments, a web-based interface can present information to anoperator to further detail the relationships between various antigensfor a cell phenotype. FIG. 21A is an exemplary developmental tree(similar in form to a geological tree) illustrating relationshipsbetween CD members (i.e. antigens) for a cell phenotype. Such adevelopmental tree can illustrate the parent-child relationships betweenantigens, and thereby further illustrate sibling relationships, cousinrelationships, and aunt relationships between antigens. In some aspects,antigens can be identified as developing along more than onedevelopmental path, and can be identified as a “twin antigen” along withoccurrences of the same antigen on the developmental tree. In furtherembodiments, the selection or transient highlighting (e.g. hovering overa field defined by software to represent a CD member or antigen) of aparticular antigen field can be used as a stimulus to further displayexpression relationships with other antigens on the developmental tree.For example, the selection or transient highlighting of an antigen fieldcan indicate, for the indicated antigen, which other antigens on thedevelopmental tree are mutually excluded from expression with theselected antigen, which other antigens on the developmental tree havecorrelating expression with the selected antigen, which other antigenson the developmental tree have inversely correlated expression with theselected antigen, which other antigens on the developmental tree aretwins to the selected antigen, or which other antigens on thedevelopmental tree otherwise coexpress with the selected antigen. Inother aspects, the developmental relationship between antigens can beprovided in a listing or tabular form, as illustrated in FIG. 21B, as anexemplary embodiment.

Multicolor Compensation with Measured Panel

As noted above, the methodology to predict and simulate panel expressioncan also be applied to derive or extrapolate the magnitudes and sourcesof signal from measured panel and antigens in a sample. Distortion andcoexpression matrices can be calculated and used to compensate formutual coexpression or patent-descendant spillover, which in aspects maybe enforced by setting appropriate gating parameters. In some aspects,the lasers used to excite dyes coupled to antibodies may excite morethan an intended dye target, and compensation tables or matrices can beused to accommodate and correct for such spillover. FIG. 22A depictsaspects of intralaser compensation according to some embodiments.Similarly, FIG. 22B depicts aspects of interlaser compensation accordingto embodiments. Both FIG. 22A and FIG. 22B depict the same compensationtable 2200, which sets forth a configuration of fluorochromes in thecolumns 2202 and a set of PMT in the rows 2204. The compensation table2200 is determined by the individual identities of the fluorochromes inthe columns 2212 and the PMT in the rows 2204, which is specific to eachhardware configuration and panel design chosen. FIG. 22A focuses on theintralaser compensation values, values for which spillover from onefluorochrome triggered by a given excitation laser wavelength can affecta PMT for a separate fluorochrome also triggered by the same excitationlaser wavelength. In contrast, FIG. 22B focuses on the interlasercompensation values, values for which spillover from one fluorochrometriggered by a given excitation laser wavelength can affect a PMT for aseparate fluorochrome triggered by a different excitation laserwavelength.

As illustrated in FIG. 22A, fluorochromes triggered by excitation lighthaving a wavelength (λ) of 405 nm are located in the FL9 and FL10columns, with corresponding detection channels in the FL9 and FL10 rows.As shown, the FL9 and FL10 detection are for Pacific Blue and PacificOrange fluorochromes, respectively. The 405 nm comparison plot 2206reflects the region of overlap each fluorochrome, and is represented inthe compensation table 2200 by the intersection region of FL9 and FL10columns and rows. The intersection region of FL9 and FL10 columns androws is populated with values or factors used to correct for distortion,as discussed above. Similarly, fluorochromes triggered by excitationlight having a wavelength (λ) of 638 nm are located in the FL6, FL7, andFL8 columns, with corresponding detection channels in the FL6, FL7, andFL8 rows. As shown, the FL6, FL7, and FL8 detection are for APC, APC-Cy5or APC-A700, and APC-Cy7 or APC-A750 fluorochromes, respectively. The638 nm comparison plot 2208 reflects the region of overlap eachfluorochrome, and is represented in the compensation table 2200 by theintersection region of FL6, FL7, and FL8 columns and rows. Theintersection region of FL6, FL7, and FL8 columns and rows is populatedwith values or factors used to correct for distortion, as discussedabove. Further, fluorochromes triggered by excitation light having awavelength (λ) of 488 nm are located in the FL1, FL2, FL3, FL4, and FL5columns, with corresponding detection channels in the FL1, FL2, FL3,FL4, and FL5 rows. As shown, the FL1, FL2, FL3, FL4, and FL5 detectionare for FITC, PE, ECD, PC5 or PC5.5, and PC7 fluorochromes,respectively. The 488 nm comparison plot 2210 reflects the region ofoverlap each fluorochrome, and is represented in the compensation table2200 by the intersection region of FL1, FL2, FL3, FL4, and FL5 columnsand rows. The intersection region of FL1, FL2, FL3, FL4, and FL5 columnsand rows is populated with values or factors used to correct fordistortion, as discussed above.

As one would expect, the intersection between a given fluorochrome andthe channel that is configured to detect that fluorochrome does notrequire any compensation value or factor, and is indicated on thecompensation table 2200 as the diagonal without any numerical valuespopulating the cells of the compensation table 2200.

As illustrated in FIG. 22B, fluorochromes can also be triggered byexcitation light at a wavelength not configured or intended to excitethe fluorochrome. The interlaser compensation region 2212 can includethe two sections of the compensation table 2200 outside of theintralaser compensation regions. The two sections of interlasercompensation region 2212 indicate compensation values or factors for:fluorochromes FL1-FL5, affecting the detection channels for FL6-FL8 andFL9-FL10; fluorochromes FL6-FL8, affecting the detection channels forFL1-FL5 and FL9-FL10; and fluorochromes FL9-FL10, affecting thedetection channels for FL1-FL5 and FL6-FL8. The two sections ofinterlaser compensation region 2212 is populated with values or factorsused to correct for distortion, as discussed above.

FIG. 23 depicts an approach to determining a distortion calculationaccording to some embodiments. In particular, FIG. 23 displays sixresult plots of fluorochrome expression measured using ten (10) dyes fora [LEUKO] cell phenotype. The table 2301 provides a listing ofantibody-dye conjugates that can be detected and used to develop gatingparameters and related plots, where FIG. 23 provides an exemplaryselection of plots and gating techniques based on the listing ofantibody-dye conjugates in table 2301. The plot 2302 displays the signalfrom a CD45-KrO antibody-dye conjugate against the general sidescattered light (SSC) measured from a flow cytometry system. In the plot2302, the expression signal in the first decade is identified as[LEUKO], relating to that phenotype. The remaining five plots are detailextrapolations, applying gating techniques, of signal in the firstdecade of [LEUKO] signal. The plot 2304 displays the SSC signal againstCD13-PC5.5 (the signal of CD13 measured in the PMT for PC5.5) andidentifies the expression region and density for CD13 therein. The plot2306 displays the SSC signal against HLADR-PC7 (the signal of HLADRmeasured in the PMT for PC7) and identifies the expression region anddensity for HLADR therein. The plot 2308 displays the SSC signal againstCD117-APC (the signal of CD117 measured in the PMT for APC) andidentifies the expression region and density for CD117 therein. The plot2310 displays the SSC signal against CD34-APCA700 (the signal of CD34measured in the PMT for APCA700) and identifies the expression regionand density for CD34 therein. The plot 2312 displays the SSC signalagainst CD33-APCA750 (the signal of CD33 measured in the PMT forAPCA750) and identifies the expression region and density for CD33therein. As evident from the six plots of FIG. 23, the result profilecan appear different for any given antibody-dye conjugate measured for,and the density of any given antibody-dye conjugate can be isolated fromsignal for other antibody-dye conjugate combinations present in the cellor phenotype.

FIG. 24 depicts an approach to determining a distortion calculationaccording to some embodiments. In particular, FIG. 24 displays sixresult plots of fluorochrome expression measured using ten (10) dyes fora [LEUKO] cell phenotype, indicating quadrants for predicted detectionlevels for different gating parameters. The table 2401 provides alisting of antibody-dye conjugates that can be detected and used todevelop gating parameters and related plots, where FIG. 24 provides anexemplary selection of plots and gating techniques based on the listingof antibody-dye conjugates in table 2401 The first plot 2402 displaysthe overall SSC signal against signal measured from a CD34-APCA700antibody-dye conjugates for a [LEUKO] cell phenotype. The signalindicating the population of CD34 positive events is identified as theregion 2414. The remaining five plots reflect application of gatingtechniques to identify positivity or negativity of signal in comparisonto signal measured by the system from other fluorochrome conjugates. Theplot 2404 displays the signal measured from CD34-APCA700 with gatingparameters applied to further indicate positive signal measured fromCD13 antigens, while remaining negative for CD33 and CD117 antigens. Theplot 2406 displays the signal measured from CD34-APCA700 with gatingparameters applied to further indicate positive signal measured fromCD17 antigens, while remaining negative for CD13 and CD33 antigens. Theplot 2408 displays the signal measured from CD34-APCA700 with gatingparameters applied to further indicate positive signal measured fromCD33 antigens, while remaining negative for CD13 and CD117 antigens. Theplot 2410 displays the signal measured from CD34-APCA700 with gatingparameters applied to further indicate positive signal measured fromCD13 and CD 33 antigens, while remaining negative for CD117 antigens.The plot 2412 displays the signal measured from CD34-APCA700 with gatingparameters applied to further indicate positive signal measured fromCD13, CD33 and CD117 antigens.

FIG. 25 depicts an approach to determining a distortion calculationaccording to some embodiments. In particular, FIG. 25 displays sixresult plots of fluorochrome expression measured using ten (10) dyes fora [LEUKO] cell phenotype, indicating quadrants for predicted detectionlevels for different gating parameters. The table 2501 provides alisting of antibody-dye conjugates that can be detected and used todevelop gating parameters and related plots, where FIG. 25 provides anexemplary selection of plots and gating techniques based on the listingof antibody-dye conjugates in table 2501. The plot 2502 displays theoverall SSC signal against signal measured from a CD117-APC antibody-dyeconjugate for a [LEUKO] cell phenotype. The signal indicating thepopulation of CD117 positive events is identified as the region 2514.The remaining five plots reflect application of gating techniques toidentify positivity or negativity of signal in comparison to signalmeasured by the system from other fluorochrome conjugates. The plot 2504displays the signal measured from CD117-APC with gating parametersapplied to further indicate positive signal measured from CD13 antigens,while remaining negative for CD33 and CD34 antigens. The plot 2506displays the signal measured from CD117-APC with gating parametersapplied to further indicate positive signal measured from CD33 antigens,while remaining negative for CD13 and CD34 antigens. The plot 2508displays the signal measured from CD117-APC with gating parametersapplied to further indicate positive signal measured from CD34 antigens,while remaining negative for CD13 and CD33 antigens. The plot 2510displays the signal measured from CD117-APC with gating parametersapplied to further indicate positive signal measured from CD13 and CD 33antigens, while remaining negative for CD34 antigens. The plot 2512displays the signal measured from CD117-APC with gating parametersapplied to further indicate positive signal measured from CD13, CD33 andCD43 antigens.

FIG. 26 depicts an approach to determining a distortion calculationaccording to some embodiments. In particular, FIG. 26 displays sevenresult plots of fluorochrome expression measured using ten (10) dyes fora [LEUKO] cell phenotype, indicating quadrants for predicted detectionlevels for different gating parameters. The table 2601 provides alisting of antibody-dye conjugates that can be detected and used todevelop gating parameters and related plots, where FIG. 26 provides anexemplary selection of plots and gating techniques based on the listingof antibody-dye conjugates in table 2601. The plot 2602 displays theoverall SSC signal against signal measured from a CD33-APCA750antibody-dye conjugate for a [LEUKO] cell phenotype. The signalindicating the population of CD33 positive events is identified as theregion 2616. The remaining six plots reflect application of gatingtechniques to identify positivity or negativity of signal in comparisonto signal measured by the system from other fluorochrome conjugates. Theplot 2604 displays the signal measured from CD33-APCA750 with gatingparameters applied to further indicate positive signal measured fromCD13 antigens, while remaining negative for CD34, CD117, and HLADRantigens. The plot 2606 displays the signal measured from CD33-APCA750with gating parameters applied to further indicate positive signalmeasured from CD34 antigens, while remaining negative for CD13, CD117,and HLADR antigens. The plot 2608 displays the signal measured fromCD33-APCA750 with gating parameters applied to further indicate positivesignal measured from CD117 antigens, while remaining negative for CD13,CD34, and HLADR antigens. The plot 2610 displays the signal measuredfrom CD33-APCA750 with gating parameters applied to further indicatepositive signal measured from HLADR antigens, while remaining negativefor CD13, CD34, and CD117 antigens. The plot 2612 displays the signalmeasured from CD33-APCA750 with gating parameters applied to furtherindicate positive signal measured from CD13, and HLADR antigens, whileremaining negative for CD34 and CD117 antigens. The plot 2614 displaysthe signal measured from CD33-APCA750 with gating parameters applied tofurther indicate positive signal measured from CD34, CD117, and HLADRantigens, while remaining negative for CD13.

FIG. 27 depicts aspects of a model of APCA700/A750 distortion in an APCchannel, according to embodiments. The three-dimensional graph plotsincreasing intensity of APCA750 signal detected by an APC channel PMT,versus increasing intensity of APCA700 signal detected by the APCchannel PMT, both plotted against the overall detection level measuredby the APC channel PMT. At a selected reference point, for example,above 1.00 as detected by the APC channel, the event signals arepredicted or calculated to be positive events. Conversely, below theselected reference point, the event signals are predicted or calculatedto be negative events. At a point of maximum APCA750 intensity and zeroAPCA700 intensity, the event signal as measured by the APC channel issubject to “pure” or solely APCA750 distortion. Conversely, at a pointof maximum APCA700 intensity and zero APCA750 intensity, the eventsignal as measured by the APC channel is subject to “pure” or solelyAPCA700 distortion. The model as shown in FIG. 27 can be expanded infurther dimensions for an arbitrary number of distorting fluorochromes.Such models as shown in FIG. 27 can be used in calculations to removethe effect of event signal from distorting fluorochromes on themeasurements recorded by a channel and PMT for a desired fluorophore.

FIG. 28 depicts an exemplary distortion table 2800, similar to thedistortion table 2200 discussed in relation to FIG. 22A and FIG. 22B. Ascan be seen in distortion table 2800, the specific dyes used by a panel(or, alternatively, used in a panel design simulation) can bespecifically identified. Similarly, the bandpass filter properties ofthe PMT channel detectors of the hardware used by an instrumentmeasuring emission (or, alternatively, used in a panel designsimulation) can be specifically identified. In aspects, the grouping ofdyes of PMT channels can be based on the wavelength of the excitationlaser for a given dye, however, the display of a distortion table canpresent the groupings of distortion values in any order that is usefulor appropriate for an operator. As is evident in comparing the twodistortion tables, distortion table 2200 and distortion table 2800, theidentity of the PMT detectors and dyes used for any particular hardwareconfiguration or panel design can change the distortion values used tomake correction calculations.

FIG. 29 is a table 2900 identifying an exemplary arrangement of targetantigens 2902, dyes 2904, and excitation lasers 2906 for activating theidentified dyes. As indicated, the excitation lasers 2906 are operativeto excite their respective dyes 2904, which are in turn conjugated withtheir target antigens 2902. In aspects, compensated data that isacquired using such arrangements can be exported to a processor andoperable system or program, such as a 20 bit table to Excel, viasoftware such as Kaluza 1.2 beta. Positive and negative eventclassification can be calculated for each event individually,particularly in the context of antibody-dye conjugates specificallyevaluated. FIGS. 29A-29E depict aspects of real data according toembodiments, using the arrangement of target antigens 2902, dyes 2904,and excitation lasers 2906 as set forth in FIG. 29.

FIG. 29A depicts aspects of real data according to an embodiment of thepresent disclosure, particularly the plotting of event data acquiredfrom a flow cytometry instrument, applying gating techniques forCD62L-FITC versus CD4-PacBlue event signal 2902. The plotting ofCD62L-FITC versus CD4-PacBlue (Pacific Blue) event signal 2902 showsdifferentiation that is orthogonal, reflective of the untouched channelsevaluated. Accordingly, the population of all events can be classifiedas distinct populations of events that are: a negative for bothCD62L-FITC and CD4-PacBlue population 2904, a positive for CD62L-FITCand negative for CD4-PacBlue population 2906, a negative for bothCD62L-FITC and a positive for CD4-PacBlue population 2908, and apositive for both CD62L-FITC and CD4-PacBlue population 2910.

FIG. 29B depicts aspects of real data according to embodiments of thepresent disclosure, particularly the plotting of event data acquiredfrom a flow cytometry instrument, applying gating techniques forCD117-PE versus CD45RA-ECD event signal 2912. Accordingly, thepopulation of all events can be classified as populations of events thatare: a negative for both CD117-PE and CD45RA-ECD population 2914, apositive for CD117-PE and negative for CD45RA-ECD population 2916, anegative for both CD117-PE and a positive for CD45RA-ECD population2918, and a positive for both CD117-PE versus CD45RA-ECD population2920. The plotting of CD117-PE versus CD45RA-ECD event signal 2912indicates at least one “hinge” that affects classification of individualevents as positive or negative, reflective of the channels evaluated. Afirst hinge can be identified as in the region between the population ofevents 2914 and the population of events 2920, while a second hinge canbe identified in the region between the population of events 2916 andthe population of events 2920. These results can further reflectmultiple superpositions of coefficients of variation (CV) betweenvarious populations.

FIG. 29C depicts aspects of real data according to embodiments of thepresent disclosure particularly the plotting of event data acquired froma flow cytometry instrument, applying gating techniques for CD69-PC5versus CD45RA-ECD event signal 2922. Accordingly, the population of allevents can be classified as populations of events that are: a negativefor both CD69-PC5 and CD45RA-ECD population 2924, a positive forCD69-PC5 and negative for CD45RA-ECD population 2926, a negative forboth CD69-PC5 and a positive for CD45RA-ECD population 2928, and apositive for both CD69-PC5 versus CD45RA-ECD population 2930. Theplotting of CD69-PC5 versus CD45RA-ECD event signal 2922 indicates atleast one “hinge” that affects classification of individual events aspositive or negative, reflective of the channels evaluated. A firsthinge can be identified as in the region between the population ofevents 2924 and the population of events 2930, while a second hinge canbe identified in the region between the population of events 2926 andthe population of events 2930. These results can further reflectmultiple superpositions of coefficients of variation (CV) betweenvarious populations.

FIG. 29D depicts aspects of real data according to embodiments of thepresent disclosure particularly the plotting of event data acquired froma flow cytometry instrument, where the application of gating techniquesmay not be useful or possible. In particular, the plotting of event dataacquired from a flow cytometry instrument for CD69-PC5 versusCD16-APCA750 event signal 2932 is shown. Accordingly, the population ofall events can be classified as populations of events that are: anegative for both CD69-PC5 and CD16-APCA750 population 2934, a positivefor CD69-PC5 and negative for CD16-APCA750 population 2936, a negativefor both CD69-PC5 and a positive for CD16-APCA750 population 2938, and apositive for both CD69-PC5 versus CD16-APCA750 population 2940. Theplotting of CD69-PC5 versus CD16-APCA750 event signal 2932, however,does not indicate a clear region that can define a “hinge” to reliablyclassify individual events as positive or negative relative to thechannels evaluated. These results can reflect multiple superpositions ofcoefficients of variation (CV) between the various populations. Thus, insome aspects, analysis of CD69-PC5 versus CD16-APCA750 in combinationmay be considered ungateable, and can be avoided in further analysis orpanel design.

FIG. 29E depicts aspects of real data according to embodiments of thepresent disclosure particularly the plotting of event data acquired froma flow cytometry instrument, where the application of gating techniquesmay not be useful or possible. In particular, the plotting of event dataacquired from a flow cytometry instrument for CD69-PC5 versus CD56-APCevent signal 2942 is shown. Accordingly, the population of all eventscan be classified as populations of events that are: a negative for bothCD69-PC5 and CD56-APC population 2944, a positive for CD69-PC5 andnegative for CD56-APC population 2946, a negative for both CD69-PC5 anda positive for CD56-APC population 2948, and a positive for bothCD69-PC5 versus CD56-APC population 2950. The plotting of CD69-PC5versus CD56-APC event signal 2942, however, does not indicate a clearregion that can define a “hinge” to reliably classify individual eventsas positive or negative relative to the channels evaluated. Theseresults can reflect multiple superpositions of coefficients of variation(CV) between the various populations. Thus, in some aspects, analysis ofCD69-PC5 versus CD56-APC in combination may be considered ungateable,and can be avoided in further analysis or panel design.

FIG. 30 is a table 3000 identifying an exemplary arrangement of targetantigens 3002, dyes 3004, and excitation lasers 3006 for activating theidentified dyes. As indicated, the excitation lasers 3006 are operativeto excite their respective dyes 3004, which are in turn conjugated withtheir target antigens 3002.

FIG. 30A depicts aspects of real data according to embodiments of thepresent disclosure, using the arrangement of target antigens 3002, dyes3004, and excitation lasers 3006 as set forth in FIG. 30, particularlythe plotting of event data acquired from a flow cytometry instrument. InFIG. 30A, gating techniques or algorithms are not applied, allowing fora holistic evaluation of positive and negative classification based onthe PMT detection channels used to evaluate the event signals.Specifically, the plotting of event data acquired from a flow cytometryinstrument for CD27-PC7 versus CD3-PacO (Pacific Orange) event signal3008 is shown. The population of all events can be classified aspopulations of events that are: a negative for both CD27-PC7 andCD3-PacO population 3010, a positive for CD27-PC7 and negative forCD3-PacO population 3012, a negative for both CD27-PC7 and a positivefor CD3-PacO population 3014, and a positive for both CD27-PC7 versusCD3-PacO population 3016. However, without application of a gatingtechnique as presented in the present disclosure, a population of eventsignals from a different antigen-antibody conjugate may incorrectly begrouped as positive for another event signal population. For example, inFIG. 30A, an event signal population positive for CD19-PC5.5 3018 can bemistakenly grouped with the event signal positive for CD27-PC7 andnegative for CD3-PacO 3012. These results again can reflect multiplesuperpositions of coefficients of variation (CV) between the variouspopulations, and further reflect the errors that may arise without theapplication of gating techniques.

Various principles can be used and applied when predicting ordetermining detection limits. For example, according to someembodiments, bright dyes may work well with weakly expressed antigens,whereas both dim and bright dyes may work well with strongly expressedantigens. Further, untouched channels may work well with weaklyexpressed antigens, whereas silent channels may work well with stronglyexpressed antigens. In some cases, it may be desirable to allow forspillover between excluding antigens. In some cases, it may be desirableto avoid spillover between non-exclusively coexpressed markers. In somecases, it may be desirable to allow for spillover from subpopulationmarkers to parent markers. In some cases, it may be desirable to avoidspillover from parent markers to subpopulation markers or co-expressedmarkers to subpopulation or other co-expressed markers if the aim is todiscriminate within the positive range, i.e. to discriminate prominentpositive versus dim positive events. In some cases, it may be desirableto minimize the number of distorting fluorochromes per detectionchannel.

Various embodiments of the present disclosure are considered as follows.In aspects, the present disclosure is directed toward a method ofdetermining a probe panel for analyzing a biological sample in a flowcytometry procedure, where the method includes: receiving informationregarding a roster comprising a plurality of probes, the individualprobes of the roster being associated with respective individualchannel-specific detection limits; receiving information regarding anantigenic coexpression pattern; evaluating combinations of individualprobes as the probe panel, based on the flow cytometer hardwareconfiguration, the individual channel-specific detection limits, and theantigenic coexpression pattern, the combinations being subsets of probesfrom the roster; determining the probe panel for use with the flowcytometer hardware configuration, individual channel-specific detectionlimits, and antigenic coexpression pattern; and outputting a probe panelfor use in a flow cytometry procedure. In some aspects, the method canfurther include receiving information regarding a flow cytometerhardware configuration. In other aspects, information received regardingthe flow cytometer hardware configuration can include any or all ofinformation regarding at least one excitation laser intensity, at leastone excitation laser wavelength, and at least one photomultiplier tubedetection channel range. In further aspects, information receivedregarding the roster comprising a plurality of probes further caninvolve any or all of accessing a non-transitory computer-readablemedium having a library of channel-specific detection limits for theplurality of probes, an operator selecting an antibody and acorresponding dye as at least one member of the probe panel,automatically selecting an antibody and a corresponding dye from alibrary as at least one member of the probe panel, and a automaticallyselecting an antibody and a corresponding dye from a library for eachmember of the probe panel. In some embodiments, the roster can includeone or more dummy members. In some aspects, receiving informationregarding the antigenic coexpression pattern can include accessing anon-transitory computer-readable medium having a library of coexpressionrelationships. In yet further aspects, evaluating combinations ofindividual probes as the probe panel can include calculating any overlapor distortion between channel-specific detection limits of two or moreindividual probes. In some aspects, the antigenic coexpression patterncan include coexpression relationships between antigens for a particularcell type.

Further embodiments of the present disclosure can be directed toward asystem for determining a probe panel for analyzing a biological samplein a flow cytometry procedure, where the system can include: aninformation input device; a flow cytometer with a hardware configurationhaving at least one excitation laser and at least one photomultipliertube detector; a probe library stored in a database, where individualprobes of the library are associated with respective individualchannel-specific detection limits; an antigenic coexpression patternstored in the database; a processor configured to evaluate a roster ofindividual probes selected from the probe library based on the flowcytometer hardware configuration, the channel-specific detection limitsof the individual probes, and the antigenic coexpression pattern; and anoutput device providing a determination of detection limits for theprobe panel, the probe panel comprising a subset of individual probesfrom the roster. In aspects, the flow cytometer hardware configurationof the system can include up to ten photomultiplier tube detectors,although in other embodiments the flow cytometer hardware configurationcan have more than ten photomultiplier tube detectors. In other aspects,the flow cytometer hardware configuration of the system can also includeup to four excitation lasers, although in other embodiments the flowcytometer hardware configuration can have more than four excitationlasers. In some aspects, wherein the information input device can beconfigurable to allow any or all of: an operator to select individualprobes from the probe library for evaluation in the roster, an operatorto input channel-specific detection limits for individual probes intothe probe library, the processor to automatically select an antibody anda corresponding dye from the probe library for each member of the probepanel. In further aspects, the processor evaluating combinations cancalculate any overlap or distortion between channel-specific detectionlimits of two or more individual probes.

Further embodiments of the present disclosure are directed toward amethod of analyzing a biological sample in a flow cytometry procedure,where the method includes: measuring the light output of a plurality ofprobes in a biological sample with a flow cytometer; receivinginformation regarding a flow cytometer hardware configuration; receivinginformation regarding a roster comprising a plurality of probes used inthe biological sample, the individual probes of the roster beingassociated with respective individual channel-specific detection limits;receiving information regarding an antigenic coexpression pattern;determining a positivity criteria and a negativity criteria for eachindividual probe in the roster and determining gating parameters for theroster, based on the flow cytometer hardware configuration, theindividual channel-specific detection limits, and the antigeniccoexpression pattern; and evaluating the light output of the pluralityof probes in the biological sample according to the positivity criteria,negativity criteria, and gating parameters. In aspects, receivinginformation regarding the flow cytometer hardware configuration furtherincludes receiving information regarding at least one excitation laserintensity, at least one excitation laser wavelength, and at least onephotomultiplier tube detection channel range. In some aspects, receivinginformation regarding the roster comprising a plurality of probes canfurther include either or both of accessing a non-transitorycomputer-readable medium having a library of channel-specific detectionlimits for the plurality of probes and an operator selecting an antibodyand a corresponding dye for the plurality of probes. In further aspects,the roster can include one or more dummy members. In some aspects,receiving information regarding the antigenic coexpression patternfurther can include accessing a non-transitory computer-readable mediumhaving a library of coexpression relationships. In other aspects, theevaluation of combinations of individual probes as the probe panel caninclude calculating any overlap or distortion between channel-specificdetection limits of two or more individual probes. In yet other aspects,the antigenic coexpression pattern can include coexpressionrelationships between antigens for a particular cell type.

Further embodiments of the present disclosure can be directed toward asystem for analyzing a biological sample in a flow cytometry procedure,where the system can include: an operator input device, though which aroster comprising a plurality of probes used in a biological sample canbe input; a flow cytometer hardware configuration having at least oneexcitation laser and at least one photomultiplier tube detector; a probelibrary stored in a database, where individual probes of the library areassociated with respective individual channel-specific detection limits;an antigenic coexpression pattern stored in the database; a processorconfigured to evaluate light emitted by the plurality of probes anddetected by the at least one photomultiplier tube detector, calculateany overlap and distortion between channel-specific detection limits theplurality of probes; and an output device providing a determination thepresence or absence of a probe in the biological sample. In someaspects, the system can have a flow cytometer hardware configurationthat includes up to ten photomultiplier tube detectors. In otheraspects, the system can have a flow cytometer hardware configurationthat includes up to four excitation lasers. In further aspects, thesystem can include an operator input device that is configurable toallow an operator to select individual probes from the probe library forevaluation in the roster, or that is configurable to allow an operatorto input channel-specific detection limits for individual probes intothe probe library.

Further embodiments of the present disclosure can be directed toward amethod for determining a probe panel for analyzing a biological samplein a flow cytometry procedure including: providing a selection of flowcytometry hardware configurations; providing a plurality of selectionsof antibody pairings; providing a selection of at least one targetpopulation; providing a selection for a plurality of antibodies, andproviding at least one selection to indicate if an antibody of theplurality of antibodies is an antigen of interest or an unrelatedantigen; providing a plurality of a selection of antigen-antibodypairings, and for an individual antigen-antibody paring, providing aselection of antigens to which the individual antigen-antibody paring ismutually excluding; providing a plurality of a selection ofantigen-antibody pairings, and for an individual antigen-antibodyparing, providing a selection of antigens that are developmentaldescendants of the individual antigen-antibody paring; providing aselection of adjustable antigen density parameters; and responsive tothe selection of a selection of flow cytometry hardware configurations,a plurality of selections of antibody pairings, a selection of at leastone target population, a selection for a plurality of antibodies, aselection of antigens to which at least one antigen-antibody paring ismutually excluding, a selection of antigens that are developmentaldescendants of at least one antigen-antibody paring, and a selection ofadjustable antigen density parameters, providing a display of detectionlimit estimates for the probe panel. In embodiments, providing aplurality of selections of antibody pairings can further includeproviding a selection of selectivity for each antibody pairing andproviding a section of dye for each antibody paring. In aspects,providing a selection of at least one target population can includeproviding a selection corresponding to a phenotype of the targetpopulation. In some aspects, providing a selection of at least onetarget population is configurable to allow for the addition or removalof target populations. In some aspects, the selection of antigens towhich an individual antigen-antibody paring is mutually excluding can beautomatically determined by a mutual exclusion database, and theselection of antigens to which an individual antigen-antibody paring ismutually excluding is automatically selected. In other aspects, aselection of antigens that are developmental descendants of individualantigen-antibody paring can be automatically determined by adevelopmental family pattern database, and the selection of antigensthat are developmental descendants of the individual antigen-antibodyparing is automatically selected. In some aspects, the providedadjustable antigen density parameters can include a selection todiscriminate in the display between positive and negative detectionlimit estimates, while in other aspects, the provided adjustable antigendensity parameters can include a selection to discriminate in thedisplay between bright positive and dim positive detection limitestimates. In further aspects, the provided adjustable antigen densityparameters include a selection to display either discrete or modulateddetection limit estimates, while in yet further aspects, the providedadjustable antigen density parameters include a selection to scale thedisplay according to an estimated detection limit of the probe panel.

Each of the calculations or operations described herein may be performedusing a computer, communication network, or other processor havinghardware, software, and/or firmware. An exemplary system with attendantcomputer, communication network, or other processor having hardware,software, and/or firmware can be found in U.S. patent application Ser.No. 13/935,154, which is hereby incorporated by reference. The variousmethod steps may be performed by modules, and the modules may compriseany of a wide variety of digital and/or analog data processing hardwareand/or software arranged to perform the method steps described herein.The modules optionally comprising data processing hardware adapted toperform one or more of these steps by having appropriate machineprogramming code associated therewith, the modules for two or more steps(or portions of two or more steps) being integrated into a singleprocessor board or separated into different processor boards in any of awide variety of integrated and/or distributed processing architectures.These methods and systems will often employ a tangible media embodyingmachine-readable code with instructions for performing the method stepsdescribed above. Suitable tangible media may comprise a memory(including a volatile memory and/or a non-volatile memory), a storagemedia (such as a magnetic recording on a floppy disk, a hard disk, atape, or the like; on an optical memory such as a CD, a CD-R/W, aCD-ROM, a DVD, or the like; or any other digital or analog storagemedia), or the like.

It is appreciated that a flow cytometry system as described herein canbe configured to carry out various aspects of methods of the presentinvention. For example, a processor component or module of a system canbe a microprocessor control module configured to receive cellularparameter signals from a sensor input device or module, from a userinterface input device or module, and/or from an analyzer system,optionally via an analyzer system interface and/or a network interfaceand a communication network. In some instances, sensor input device(s)may include or be part of a cellular analysis system such as a flowcytometer. In some instances, user interface input device(s) and/ornetwork interface may be configured to receive cellular parametersignals generated by a cellular analysis system such as a flowcytometer. In some instances, analyzer system may include or be part ofa cellular analysis system such as a flow cytometer.

Processor component or module can also be configured to transmitcellular parameter signals, optionally processed according to any of thetechniques disclosed herein, to a sensor output device or module, to auser interface output device or module, to a network interface device ormodule, to an analyzer system interface, or any combination thereof.Each of the devices or modules according to embodiments of the presentinvention can include one or more software modules on a computerreadable medium that is processed by a processor, or hardware modules,or any combination thereof. Any of a variety of commonly used platforms,such as Windows, Macintosh, and Unix, along with any of a variety ofcommonly used programming languages, may be used to implementembodiments of the present invention.

User interface input devices may include, for example, a touchpad, akeyboard, pointing devices such as a mouse, a trackball, a graphicstablet, a scanner, a joystick, a touchscreen incorporated into adisplay, audio input devices such as voice recognition systems,microphones, and other types of input devices. User input devices mayalso download a computer executable code from a tangible storage mediaor from communication network, the code embodying any of the methods oraspects thereof disclosed herein. It will be appreciated that terminalsoftware may be updated from time to time and downloaded to the terminalas appropriate. In general, use of the term “input device” is intendedto include a variety of conventional and proprietary devices and ways toinput information into module system.

User interface output devices may include, for example, a displaysubsystem, a printer, a fax machine, or non-visual displays such asaudio output devices. The display subsystem may be a cathode ray tube(CRT), a flat-panel device such as a liquid crystal display (LCD), aprojection device, or the like. The display subsystem may also provide anon-visual display such as via audio output devices. In general, use ofthe term “output device” is intended to include a variety ofconventional and proprietary devices and ways to output information frommodule system to a user.

A bus subsystem can provide a mechanism for letting the variouscomponents and subsystems of module system communicate with each otheras intended or desired. The various subsystems and components of modulesystem need not be at the same physical location but may be distributedat various locations within a distributed network. A bus subsystem canbe a single bus or may utilize multiple busses.

A network interface can provide an interface to an outside network orother devices. Outside communication network can be configured to effectcommunications as needed or desired with other parties. In manyembodiments, the communication network can be a web-based or cloud-basedprocessing system, allowing for remote access and processing. It canthus receive an electronic packet from module system and transmit anyinformation as needed or desired back to module system. In addition toproviding such infrastructure communications links internal to thesystem, a communications network system may also provide a connection toother networks such as the internet and may comprise a wired, wireless,modem, and/or other type of interfacing connection.

All patents, patent publications, patent applications, journal articles,books, technical references, and the like discussed in the instantdisclosure are incorporated herein by reference in their entirety forall purposes.

It is to be understood that the figures and descriptions of theinvention have been simplified to illustrate elements that are relevantfor a clear understanding of the invention. It should be appreciatedthat the figures are presented for illustrative purposes and not asconstruction drawings. Omitted details and modifications or alternativeembodiments are within the purview of persons of ordinary skill in theart.

It can be appreciated that, in certain aspects of the invention, asingle component may be replaced by multiple components, and multiplecomponents may be replaced by a single component, to provide an elementor structure or to perform a given function or functions. Except wheresuch substitution would not be operative to practice certain embodimentsof the invention, such substitution is considered within the scope ofthe invention.

The examples presented herein are intended to illustrate potential andspecific implementations of the invention. It can be appreciated thatthe examples are intended primarily for purposes of illustration of theinvention for those skilled in the art. There may be variations to thesediagrams or the operations described herein without departing from thespirit of the invention. For instance, in certain cases, method steps oroperations may be performed or executed in differing order, oroperations may be added, deleted or modified.

Different arrangements of the components depicted in the drawings ordescribed above, as well as components and steps not shown or describedare possible. Similarly, some features and sub-combinations are usefuland may be employed without reference to other features andsub-combinations. Embodiments of the invention have been described forillustrative and not restrictive purposes, and alternative embodimentswill become apparent to readers of this patent. Accordingly, the presentinvention is not limited to the embodiments described above or depictedin the drawings, and various embodiments and modifications can be madewithout departing from the scope of the claims below.

1. A method of designing a probe panel for a flow cytometer, the methodcomprising: determining a distortion factor that quantifies spillovereffect caused by emission of a first label, intended to be measured in afirst channel, into a second channel; inputting a maximum expectedsignal of a first probe-label combination including the first label anda first probe; calculating an increase in detection limit in the secondchannel based on the distortion factor and the maximum expected signalof the first probe-label combination; and selecting a probe-labelcombination to include in the probe panel based on the calculatedincrease in detection limit. 2-33. (canceled)